GitHub Repository The Rmarkdown file can be downloaded from the Code drop down menu (top right).

This supplementary file contains the R workflow for processing and analysing the raw data, and creating figures for the manuscript titled “Global vulnerability of anurans to drought and warming”.

Abbreviations

  • AI: Aridity Index
  • EWL: Evaporative water loss
  • IUCN: International Union for Conservation of Nature
  • PDSI: Palmer Drought Severity Index
  • PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
  • RH: Relative humidity
  • SPP: Shared Socioeconomic Pathway
  • WU: water uptake

Spatial risk

Descriptors

The following series of code is used to estimate the risk of extant anurans to increasing aridity (Aridity Index; AI) and drought (Palmer Drought Severity Index; PDSI). The AI was categorised to five categories and The AI was categorised to five categories (Humid, Dry sub-humid, Semi-arid, Arid, and Hyper-arid) and the PDSI was categorised to seven categories (Extremely moist, Very moist, Moderate moist, Normal, Moderate drought, Severe drought, Extreme drought) based on descriptions from Budyko (1961) and Palmer (1965), respectively (Table S1).

Generalised ecological types or ‘ecotype’ were classified based on descriptions from Moen and Wiens (2017) focusing on adult behaviour and microhabitat preferences outside the breeding season given that many anurans breed in water but are not adapted to live in water all year (Table S2).

Table S1

Table S1 Aridity index (Budyko, 1961) and the Palmer Drought Severity Index (Palmer, 1965) categorised.

Aridity index (AI) AI value Palmer Drought Severity Index (PDSI) PDSI Value
Humid ≥ 0.65 Extremely moist ≥ 4
Dry sub-humid 0.50 – ≤ 0.65 Very moist 3 – ≤ 4
Semi-arid 0.20 – ≤ 0.50 Moderate moist 2 – ≤ 3
Arid 0.05 – ≤ 0.20 Normal -2 – ≤ 2
Hyper-arid < 0.05 Moderate drought -3 – ≤ -2
Severe drought -4 – ≤ -3
Extreme drought < -4

Table S2

Table S2 Microhabitat preference or ecotype (Moen and Wiens, 2017).

Ecotype Description
Aquatic Almost always in water.
Arboreal Ability to climb vegetation and spends a substantial amount of time above ground (e.g. forests, on top of vegetations in wetlands).
Semi-aquatic Stay near water bodies, usually permanent water (e.g. ground-level wetlands). Often observed in water and nearby vegetation.
Ground-dwelling Terrestrial only, not always near water bodies, however always in a moist environment (e.g. forest leaf litter, moist soils, scrublands, grassland). Sometimes above ground and sometimes semi-fossorial. Can be classified as broad.
Fossorial The non-breeding season is spent underground in burrows. Capable of burrowing and aestivating.
Stream-dwelling Restricted to near fast-flowing streams usually on rocks or vegetation near streams.

Species richness

Anuran (frogs and toads) distribution shape files were obtained from the International Union for Conservation of Nature’s Red List of Threatened Species (IUCN Red List; extracted on 11/01/2021). Species richness was defined as the sum of species in each grid cell (0.5°), based on the geographic range, and was calculated using the rasterizeIUCN and calcSR function from the from the rasterSp package. Ecotype-specific species richness was also calculated.

filedir <- "rasterSp/"

# Clean raw data
frog_dat <- read.csv(file.path(data_path, "anuran_species_list.csv")) %>%
    dplyr::select(genus:strategy, SVL_cm:binomial_tree_phylo) %>%
    dplyr::mutate(lnSVL = log(SVL_cm), lnMass = log(mass_g), lnArea = log(shape_area),
        IUCN = factor(IUCN, levels = c("Least Concern", "Near Threatened", "Vulnerable",
            "Endangered", "Critically Endangered", "Extinct")), order_name = factor(order_name),
        family_name = factor(family_name), ecotype = factor(ecotype)) %>%
    dplyr::filter(order_name == "ANURA" & ecotype != "" & IUCN != "" & IUCN != "Extinct") %>%
    droplevels()

# Convert shape files into rasters and save to file once (downloaded shape
# files 08/01/2020) only extracted distribution of native range anuran_rast <-
# rasterizeIUCN(dsn = paste0(filedir, 'ANURA/ANURA.shp'), resolution = 0.5,
# seasonal = c(1, 2), origin = 1, presence = c(1,2), save = TRUE, path =
# paste0(rasterSp_path))

# Calculate anuran richness and by ecotype The calcSR function uses a stepwise
# procedure to calculate the sum of species for each grid cell.
anuran_sr <- rasterSp::calcSR(species_names = frog_dat$binomial_IUCN, path = paste0(rasterSp_path))
aquatic_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Aquatic",
    "binomial_IUCN"], path = paste0(rasterSp_path))
arboreal_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Arboreal",
    "binomial_IUCN"], path = paste0(rasterSp_path))
fossorial_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Fossorial",
    "binomial_IUCN"], path = paste0(rasterSp_path))
ground_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Ground-dwelling",
    "binomial_IUCN"], path = paste0(rasterSp_path))
semi_aq_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Semi-aquatic",
    "binomial_IUCN"], path = paste0(rasterSp_path))
stream_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Stream-dwelling",
    "binomial_IUCN"], path = paste0(rasterSp_path))

# Convert raster to matrix then to data frame
anuran_sr_df <- raster::as.data.frame(raster::rasterToPoints(anuran_sr)) %>%
    dplyr::rename(species_n = layer)
aquatic_sr_df <- raster::as.data.frame(raster::rasterToPoints(aquatic_sr)) %>%
    dplyr::rename(aquatic_n = layer)
arboreal_sr_df <- raster::as.data.frame(raster::rasterToPoints(arboreal_sr)) %>%
    dplyr::rename(arboreal_n = layer)
fossorial_sr_df <- raster::as.data.frame(raster::rasterToPoints(fossorial_sr)) %>%
    dplyr::rename(fossorial_n = layer)
ground_sr_df <- raster::as.data.frame(raster::rasterToPoints(ground_sr)) %>%
    dplyr::rename(ground_n = layer)
semi_aq_sr_df <- raster::as.data.frame(raster::rasterToPoints(semi_aq_sr)) %>%
    dplyr::rename(semi_aq_n = layer)
stream_sr_df <- raster::as.data.frame(raster::rasterToPoints(stream_sr)) %>%
    dplyr::rename(stream_n = layer)

There are 5636 anuran species from the IUCN Red List used for the analysis.

Calculate AI and PDSI

High resolution (~4 km2) global dataset on precipitation (mm/month) and potential evapotranspiration (mm/month) were obtained from Abatzoglou et al. (2018) under 1) the current climate (1970–2000), 2) an intermediate greenhouse gas emission scenario of +2°C (Shared Socioeconomic Pathway 2–4.5; SPP2–4.5), and 3) a high greenhouse gas emission or “business-as-usual” scenario of +4°C (SSP5–8.5) by 2080–2099 (Fig. S1a–f).

We obtained a self-calibrated PDSI with Penman–Monteith potential evapotranspiration representing the current climate (1970–2000) and an intermediate and high emission scenario by 2080–2099 (SSP2–4.5 and SSP5-8.5) from Zhao and Dai (2022). The SSP2–4.5 and SSP5–8.5 scenarios were based on the average of 25 CMIP6 models of precipitation, evapotranspiration, soil moisture, and runoff (Eyring et al., 2016), where the mean annual surface temperature is expected to increase by 2.7°C (2.1–3.5°C range) and 4.4°C (3.3–5.7°C range), respectively by 2080–2100 (IPCC, 2021).

# Import the downloaded files
ppt_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate19812010_ppt.nc")),
    anuran_sr)  # Precipitation year 1981-2010 - Pr (mm)
pet_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate19812010_pet.nc")),
    anuran_sr)  # Evapotranspiration 1981-2010 - ET0 (mm)

# +2C and +4C future scenarios
ppt_2C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate2C_ppt.nc")),
    anuran_sr)
pet_2C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate2C_pet.nc")),
    anuran_sr)
ppt_4C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate4C_ppt.nc")),
    anuran_sr)
pet_4C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, "TerraClimate4C_pet.nc")),
    anuran_sr)

# Obtain mean values from 1981-2010
ppt_mean_rast <- raster::calc(ppt_rast, fun = mean, na.rm = TRUE)
pet_mean_rast <- raster::calc(pet_rast, fun = mean, na.rm = TRUE)
ppt_2C_mean_rast <- raster::calc(ppt_2C_rast, fun = mean, na.rm = TRUE)
pet_2C_mean_rast <- raster::calc(pet_2C_rast, fun = mean, na.rm = TRUE)
ppt_4C_mean_rast <- raster::calc(ppt_4C_rast, fun = mean, na.rm = TRUE)
pet_4C_mean_rast <- raster::calc(pet_4C_rast, fun = mean, na.rm = TRUE)

# Obtain mean values from 10-year monthly PDSI
PDSI_ssp245_rast <- raster::stack(x = file.path(spatial_path, "pdsisc.monthly.1900-2100.r2.5x2.5.EnsAvg25Models.TP2.ipe-2.ssp245.nc"))
PDSI_ssp585_rast <- raster::stack(x = file.path(spatial_path, "pdsisc.monthly.1900-2100.r2.5x2.5.EnsAvg25Models.TP2.ipe-2.ssp585.nc"))

PDSI_cur_rast <- raster::subset(PDSI_ssp245_rast, grep("X197.*|X198.*|X199.*", names(PDSI_ssp245_rast),
    value = T))
PDSI_2C_rast <- raster::subset(PDSI_ssp245_rast, grep("X207.*|X208.*|X209.*", names(PDSI_ssp245_rast),
    value = T))
PDSI_4C_rast <- raster::subset(PDSI_ssp585_rast, grep("X207.*|X208.*|X209.*", names(PDSI_ssp585_rast),
    value = T))

PDSI_cur_mean_rast <- raster::calc(PDSI_cur_rast, fun = mean, na.rm = TRUE)
PDSI_2C_mean_rast <- raster::calc(PDSI_2C_rast, fun = mean, na.rm = TRUE)
PDSI_4C_mean_rast <- raster::calc(PDSI_4C_rast, fun = mean, na.rm = TRUE)

# Calculate aridity index [Precipitation (ppt) / Evapotranspiration (pet)]
ai_rast <- raster::overlay(x = ppt_mean_rast, y = pet_mean_rast, fun = function(x,
    y) {
    return(x/y)
})
ai_2C_rast <- raster::overlay(x = ppt_2C_mean_rast, y = pet_2C_mean_rast, fun = function(x,
    y) {
    return(x/y)
})
ai_4C_rast <- raster::overlay(x = ppt_4C_mean_rast, y = pet_4C_mean_rast, fun = function(x,
    y) {
    return(x/y)
})

# Calculate change in PDSI Reproject intensity raster to match anuran_sr
PDSI_cur_mean_rast <- raster::projectRaster(PDSI_cur_mean_rast, anuran_sr)
PDSI_2C_mean_rast <- raster::projectRaster(PDSI_2C_mean_rast, anuran_sr)
PDSI_4C_mean_rast <- raster::projectRaster(PDSI_4C_mean_rast, anuran_sr)

PDSI_2C_diff_rast <- raster::overlay(x = PDSI_cur_mean_rast, y = PDSI_2C_mean_rast,
    fun = function(x, y) {
        return(y - x)
    })
PDSI_4C_diff_rast <- raster::overlay(x = PDSI_cur_mean_rast, y = PDSI_4C_mean_rast,
    fun = function(x, y) {
        return(y - x)
    })

# Convert raster to matrix then to data frame
ai_df <- raster::as.data.frame(raster::rasterToPoints(ai_rast))
ai_2C_df <- raster::as.data.frame(raster::rasterToPoints(ai_2C_rast))
ai_4C_df <- raster::as.data.frame(raster::rasterToPoints(ai_4C_rast))

PDSI_2C_diff_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_2C_diff_rast))
PDSI_4C_diff_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_4C_diff_rast))

# Convert aridity index into category
ai_df <- ai_df %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

ai_2C_df <- ai_2C_df %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

ai_4C_df <- ai_4C_df %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

# Convert PDSI to category
PDSI_2C_diff_df <- PDSI_2C_diff_df %>%
    # Recode
dplyr::mutate(change_2C = case_when(layer >= 4 ~ "4", layer >= 3 & layer < 4 ~ "3",
    layer >= 2 & layer < 3 ~ "2", layer >= 1 & layer < 2 ~ "1", layer >= -1 & layer <
        1 ~ "0", layer >= -2 & layer < -1 ~ "-1", layer >= -3 & layer < -2 ~ "-2",
    layer >= -4 & layer < -3 ~ "-3", layer < -4 ~ "-4")) %>%
    # Convert to ordered factor
dplyr::mutate(change_2C = factor(change_2C, levels = c("-4", "-3", "-2", "-1", "0",
    "1", "2", "3", "4"), ordered = TRUE))

PDSI_4C_diff_df <- PDSI_4C_diff_df %>%
    # Recode
dplyr::mutate(change_4C = case_when(layer >= 4 ~ "4", layer >= 3 & layer < 4 ~ "3",
    layer >= 2 & layer < 3 ~ "2", layer >= 1 & layer < 2 ~ "1", layer >= -1 & layer <
        1 ~ "0", layer >= -2 & layer < -1 ~ "-1", layer >= -3 & layer < -2 ~ "-2",
    layer >= -4 & layer < -3 ~ "-3", layer < -4 ~ "-4")) %>%
    # Convert to ordered factor
dplyr::mutate(change_4C = factor(change_4C, levels = c("-4", "-3", "-2", "-1", "0",
    "1", "2", "3", "4"), ordered = TRUE))
# Crop and mask
world <- rgdal::readOGR(file.path(spatial_path, "ne_50m_land/ne_50m_land.shp"))
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/ne_50m_land/ne_50m_land.shp", layer: "ne_50m_land"
## with 1420 features
## It has 3 fields
## Integer64 fields read as strings:  scalerank
world_spdf <- sp::SpatialPolygonsDataFrame(world, world@data)

# Precipitation
ppt_curr_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650), name = "Precipitation (mm)") +
    ggtitle("Current (1981-2010)") + scale_y_continuous(limits = c(-60, 90), expand = c(0,
    0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) + guides(fill = guide_colourbar(barwidth = 10,
    barheight = 0.3, label.position = "bottom")) + theme_void() + ylab(NULL) + xlab(NULL) +
    theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
        plot.title = element_text(size = 10), legend.title = element_text(size = 8),
        legend.position = "bottom") + coord_fixed(ratio = 1)

ppt_2C_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_2C_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650)) + ggtitle("+2°C (2080-2100)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme_void() + ylab(NULL) + xlab(NULL) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

ppt_4C_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_4C_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650)) + ggtitle("+4°C (2080-2100)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme_void() + ylab(NULL) + xlab(NULL) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

ppt_legend <- cowplot::get_legend(ppt_curr_plot)

ppt_prow <- cowplot::plot_grid(ppt_curr_plot + theme(legend.position = "none"), ppt_2C_plot +
    theme(legend.position = "none"), ppt_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("a", "b", "c"))

ppt_plots <- cowplot::plot_grid(ppt_prow, ppt_legend, ncol = 1, rel_heights = c(1,
    0.1))

# Evapotranspiration
pet_curr_plot <- raster::as.data.frame(raster::rasterToPoints(pet_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300), name = "Evapotranspiration (mm)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + guides(fill = guide_colourbar(barwidth = 10, barheight = 0.3,
    label.position = "bottom")) + theme_void() + ylab(NULL) + xlab(NULL) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10),
    legend.title = element_text(size = 8), legend.position = "bottom") + coord_fixed(ratio = 1)

pet_2C_plot <- raster::as.data.frame(raster::rasterToPoints(pet_2C_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300)) + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme_void() + ylab(NULL) + xlab(NULL) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

pet_4C_plot <- raster::as.data.frame(raster::rasterToPoints(pet_4C_mean_rast)) %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = layer)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300)) + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme_void() + ylab(NULL) + xlab(NULL) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

pet_legend <- cowplot::get_legend(pet_curr_plot)

pet_prow <- cowplot::plot_grid(pet_curr_plot + theme(legend.position = "none"), pet_2C_plot +
    theme(legend.position = "none"), pet_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("d", "e", "f"))

pet_plots <- cowplot::plot_grid(pet_prow, pet_legend, ncol = 1, rel_heights = c(1,
    0.1))

# Aridity Index
arid_col <- c("#8E063B", "#CB6D53", "#E99A2C", "#F5D579", "white")
ai_curr_plot <- ggplot() + geom_raster(data = ai_df, aes(y = y, x = x, fill = category)) +
    geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = arid_col, guide = guide_legend(reverse = TRUE),
    name = "Aridity Index") + ylab(NULL) + xlab(NULL) + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme_void() + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10), legend.title = element_text(size = 8),
    legend.key.size = unit(0.2, "cm"), legend.position = "bottom") + coord_fixed(ratio = 1)

ai_2C_plot <- ggplot() + geom_raster(data = ai_2C_df, aes(y = y, x = x, fill = category)) +
    geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = arid_col, guide = guide_legend(reverse = TRUE),
    name = "Aridity Index") + ylab(NULL) + xlab(NULL) + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme_void() + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)

ai_4C_plot <- ggplot() + geom_raster(data = ai_4C_df, aes(y = y, x = x, fill = category)) +
    geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = arid_col, guide = guide_legend(reverse = TRUE),
    name = "Aridity Index") + ylab(NULL) + xlab(NULL) + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme_void() + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)

ai_legend <- cowplot::get_legend(ai_curr_plot)

ai_prow <- cowplot::plot_grid(ai_curr_plot + theme(legend.position = "none"), ai_2C_plot +
    theme(legend.position = "none"), ai_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("g", "h", "i"))

ai_plots <- cowplot::plot_grid(ai_prow, ai_legend, ncol = 1, rel_heights = c(1, 0.1))

cowplot::plot_grid(ppt_plots, pet_plots, ai_plots, ncol = 1)

Fig. S1. Climate data used to calculate the aridity index. Mean precipitation (mm) for (a) the current scenario from 1981-2010, (b) under an intermediate emission scenario (Shared Socioeconomic Pathways 2 - 4.5; SSP2-4.5), and (c) under a high emission scenario (SSP5-8.5) by 2080-2100. Mean potential evapotranspiration (mm) for (d) the current scenario from 1981-2010, (e) under an intermediate emission scenario (SSP2-4.5), and (f) under a high emission scenario (SSP5-8.5) by 2080-2100. Calculated Aridity Index for (g) the current scenario from 1981-2010, (h) under an intermediate emission scenario (SSP2-4.5), and (i) under a high emession scenario (SSP5-8.5) by 2080-2100.

AI risk

To examine the relationship of species richness with aridity, the number of species per gird cell was overlapped with the aridity raster layer, where each grid was assigned an AI category. The change in species richness between the current and projected (either +2 or +4 °C warming) AI category was calculated as the change in AI category grids (resolution 0.5° for decimal degree coordinates) occupied by anurans relative to the future projection. A decrease indicates reduced number of species with the assigned AI category and vice versa.

# Merge aridity index, +4C and amphibian species richness
ai_sp_df <- ai_df %>%
    dplyr::full_join(ai_2C_df, by = c("x", "y")) %>%
    dplyr::full_join(ai_4C_df, by = c("x", "y")) %>%
    dplyr::full_join(anuran_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(aquatic_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(arboreal_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(fossorial_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(ground_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(semi_aq_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(stream_sr_df, by = c("x", "y")) %>%
    dplyr::rename(aridity = layer.x, aridity_2C = layer.y, aridity_4C = layer, category = category.x,
        category_2C = category.y, category_4C = category) %>%
    drop_na(category)

# Calculate grid cells occupied for current climate
ai_sp_sum <- data.frame(ai_sp_df %>%
    dplyr::group_by(category) %>%
    dplyr::summarise(species_n = length(species_n[!is.na(species_n)]), aquatic_n = length(aquatic_n[!is.na(aquatic_n)]),
        arboreal_n = length(arboreal_n[!is.na(arboreal_n)]), fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
        ground_n = length(ground_n[!is.na(ground_n)]), semi_aq_n = length(semi_aq_n[!is.na(semi_aq_n)]),
        stream_n = length(stream_n[!is.na(stream_n)]))) %>%
    dplyr::mutate(scenario = "Current", all_freq = species_n/sum(species_n) * 100,
        aquatic_freq = aquatic_n/sum(aquatic_n) * 100, arboreal_freq = arboreal_n/sum(arboreal_n) *
            100, fossorial_freq = fossorial_n/sum(fossorial_n) * 100, ground_freq = ground_n/sum(ground_n) *
            100, semi_aq_freq = semi_aq_n/sum(semi_aq_n) * 100, stream_freq = stream_n/sum(stream_n) *
            100)

# Calculate grid cells occupied for future climate
ai_2C_sp_sum <- data.frame(ai_sp_df %>%
    dplyr::group_by(category_2C) %>%
    dplyr::summarise(species_n = length(species_n[!is.na(species_n)]), aquatic_n = length(aquatic_n[!is.na(aquatic_n)]),
        arboreal_n = length(arboreal_n[!is.na(arboreal_n)]), fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
        ground_n = length(ground_n[!is.na(ground_n)]), semi_aq_n = length(semi_aq_n[!is.na(semi_aq_n)]),
        stream_n = length(stream_n[!is.na(stream_n)]))) %>%
    dplyr::mutate(scenario = "SSP245", all_freq = species_n/sum(species_n) * 100,
        aquatic_freq = aquatic_n/sum(aquatic_n) * 100, arboreal_freq = arboreal_n/sum(arboreal_n) *
            100, fossorial_freq = fossorial_n/sum(fossorial_n) * 100, ground_freq = ground_n/sum(ground_n) *
            100, semi_aq_freq = semi_aq_n/sum(semi_aq_n) * 100, stream_freq = stream_n/sum(stream_n) *
            100) %>%
    dplyr::rename(category = category_2C)

ai_4C_sp_sum <- data.frame(ai_sp_df %>%
    dplyr::group_by(category_4C) %>%
    dplyr::summarise(species_n = length(species_n[!is.na(species_n)]), aquatic_n = length(aquatic_n[!is.na(aquatic_n)]),
        arboreal_n = length(arboreal_n[!is.na(arboreal_n)]), fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
        ground_n = length(ground_n[!is.na(ground_n)]), semi_aq_n = length(semi_aq_n[!is.na(semi_aq_n)]),
        stream_n = length(stream_n[!is.na(stream_n)]))) %>%
    dplyr::mutate(scenario = "SSP585", all_freq = species_n/sum(species_n) * 100,
        aquatic_freq = aquatic_n/sum(aquatic_n) * 100, rboreal_freq = arboreal_n/sum(arboreal_n) *
            100, fossorial_freq = fossorial_n/sum(fossorial_n) * 100, ground_freq = ground_n/sum(ground_n) *
            100, semi_aq_freq = semi_aq_n/sum(semi_aq_n) * 100, stream_freq = stream_n/sum(stream_n) *
            100) %>%
    dplyr::rename(category = category_4C)

PDSI risk

With a monthly prediction of PDSI from 1950 to 2100 globally available from Zhao and Dai (2022), we classified future drought risk in three ways: 1) increase in drought intensity (magnitude change in PDSI), increase in drought frequency (monthly PDSI counts below -2 per year), increase in duration (number of consecutive months with PDSI values below -2). Change in drought intensity (ΔPDSI[intensity]), frequency (ΔPDSI[frequency]), and duration (ΔPDSI[duration]) under a +2 or +4 °C warming scenario (2080–2100) was calculated relative to the 1970–2000 monthly climatology per gird cell (ΔPDSI = PDSI[future] – PDSI[current]). Note, the temporal resolution was limited to monthly variation as the PDSI was developed to monitor long-term meteorological drought.

# Change in PDSI intensity
PDSI_sp_df <- anuran_sr_df %>%
    dplyr::full_join(PDSI_2C_diff_df, by = c("x", "y")) %>%
    dplyr::full_join(PDSI_4C_diff_df, by = c("x", "y")) %>%
    dplyr::full_join(aquatic_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(arboreal_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(fossorial_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(ground_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(semi_aq_sr_df, by = c("x", "y")) %>%
    dplyr::full_join(stream_sr_df, by = c("x", "y")) %>%
    dplyr::rename(PDSI_2C = layer.x, PDSI_4C = layer.y) %>%
    drop_na(change_2C) %>%
    filter(species_n != "NA")

# Calculate grid cells occupied for current climate
PDSI_sp_2C <- data.frame(PDSI_sp_df %>%
    dplyr::group_by(change_2C) %>%
    dplyr::summarise(species_n = length(species_n[!is.na(species_n)]), aquatic_n = length(aquatic_n[!is.na(aquatic_n)]),
        arboreal_n = length(arboreal_n[!is.na(arboreal_n)]), fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
        ground_n = length(ground_n[!is.na(ground_n)]), semi_aq_n = length(semi_aq_n[!is.na(semi_aq_n)]),
        stream_n = length(stream_n[!is.na(stream_n)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100, aquatic_freq = aquatic_n/sum(aquatic_n) *
        100, arboreal_freq = arboreal_n/sum(arboreal_n) * 100, fossorial_freq = fossorial_n/sum(fossorial_n) *
        100, ground_freq = ground_n/sum(ground_n) * 100, semi_aq_freq = semi_aq_n/sum(semi_aq_n) *
        100, stream_freq = stream_n/sum(stream_n) * 100)

PDSI_sp_4C <- data.frame(PDSI_sp_df %>%
    dplyr::group_by(change_4C) %>%
    dplyr::summarise(species_n = length(species_n[!is.na(species_n)]), aquatic_n = length(aquatic_n[!is.na(aquatic_n)]),
        arboreal_n = length(arboreal_n[!is.na(arboreal_n)]), fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
        ground_n = length(ground_n[!is.na(ground_n)]), semi_aq_n = length(semi_aq_n[!is.na(semi_aq_n)]),
        stream_n = length(stream_n[!is.na(stream_n)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100, aquatic_freq = aquatic_n/sum(aquatic_n) *
        100, arboreal_freq = arboreal_n/sum(arboreal_n) * 100, fossorial_freq = fossorial_n/sum(fossorial_n) *
        100, ground_freq = ground_n/sum(ground_n) * 100, semi_aq_freq = semi_aq_n/sum(semi_aq_n) *
        100, stream_freq = stream_n/sum(stream_n) * 100)
# Calculate frequency of moderate to extreme drought (<-2 PDSI) per year.

# Temporal change in PDSI intensity
PDSI_2C_time_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ssp245_rast))
PDSI_4C_time_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ssp585_rast))

# Convert wide to long
freq_2C_df <- PDSI_2C_time_df %>%
    tidyr::pivot_longer(!c("x", "y"), names_to = "dates", values_to = "PDSI") %>%
    data.frame() %>%
    tidyr::separate(dates, c("year", "months")) %>%
    dplyr::mutate(year = as.numeric(substring(year, 2))) %>%
    dplyr::filter(year >= 1950 & year != 2100)

freq_4C_df <- PDSI_4C_time_df %>%
    tidyr::pivot_longer(!c("x", "y"), names_to = "dates", values_to = "PDSI") %>%
    data.frame() %>%
    tidyr::separate(dates, c("year", "months")) %>%
    dplyr::mutate(year = as.numeric(substring(year, 2))) %>%
    dplyr::filter(year >= 1950 & year != 2100)

# Summarise average monthly counts <-2 PDSI from 1970 to 1999 or 2080 to 2100
freq_mean_df <- freq_2C_df %>%
    dplyr::filter(year >= 1970 & year <= 1999) %>%
    dplyr::group_by(x, y, year) %>%
    dplyr::summarise(count = sum(PDSI < -2)) %>%
    dplyr::group_by(x, y) %>%
    dplyr::summarise(mean_curr = mean(count))

freq_2C_diff_df <- freq_2C_df %>%
    dplyr::filter(year >= 2080 & year != 2100) %>%
    dplyr::group_by(x, y, year) %>%
    dplyr::summarise(count = sum(PDSI < -2)) %>%
    dplyr::group_by(x, y) %>%
    dplyr::summarise(mean_2C = mean(count)) %>%
    dplyr::inner_join(freq_mean_df, by = c(x = "x", y = "y")) %>%
    dplyr::mutate(diff_2C = mean_2C - mean_curr)

freq_4C_diff_df <- freq_4C_df %>%
    dplyr::filter(year >= 2080 & year != 2100) %>%
    dplyr::group_by(x, y, year) %>%
    dplyr::summarise(count = sum(PDSI < -2)) %>%
    dplyr::group_by(x, y) %>%
    dplyr::summarise(mean_4C = mean(count)) %>%
    dplyr::inner_join(freq_2C_diff_df, by = c(x = "x", y = "y")) %>%
    dplyr::mutate(diff_4C = mean_4C - mean_curr)
# Calculate duration of moderate to extreme drought (<-2 PDSI) from 1970 to
# 1999
dur_curr_df <- freq_2C_df %>%
    dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>%
    dplyr::filter(year >= 1970 & year <= 1999) %>%
    dplyr::group_by(x, y) %>%
    dplyr::summarise(mean_curr = mean(rle(bin)$lengths[rle(bin)$values == 1])) %>%
    replace(is.na(.), 0)

# Calculate duration of moderate to extreme drought (<-2 PDSI) from 2080 to
# 2100
dur_2C_df <- freq_2C_df %>%
    dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>%
    dplyr::filter(year >= 2080 & year != 2100) %>%
    dplyr::group_by(x, y) %>%
    dplyr::summarise(mean_2C = mean(rle(bin)$lengths[rle(bin)$values == 1])) %>%
    replace(is.na(.), 0) %>%
    dplyr::inner_join(dur_curr_df, by = c(x = "x", y = "y")) %>%
    dplyr::mutate(diff_2C = mean_2C - mean_curr)

dur_4C_df <- freq_4C_df %>%
    dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>%
    dplyr::group_by(x, y) %>%
    dplyr::filter(year >= 2080 & year != 2100) %>%
    dplyr::summarise(mean_4C = mean(rle(bin)$lengths[rle(bin)$values == 1])) %>%
    replace(is.na(.), 0) %>%
    dplyr::inner_join(dur_2C_df, by = c(x = "x", y = "y")) %>%
    dplyr::mutate(diff_4C = mean_4C - mean_curr)

The simultaneous risk of drought intensity, frequency, and duration within a grid cell that are occupied by anurans was calculated by converting each risk category as binary. Gird cells with a ΔPDSI[intensity] below -1 (indicating a decrease in PDSI) were assigned a ‘1’ binary. Both ΔPDSI[frequency] and ΔPDSI[duration] were assigned a binary of ‘1’ if the grid cell has a value of 1 month or higher (indicating increase in frequency or duration relative to current scenario). The number of overlapping binaries were summed up per grid cell. Therefore, a risk factor of 2 indicate anurans in the assigned grid cell are at increasing risk of two drought events. We estimated which species assemblages were risk to drought using an arbitrary risk factor (species richness × drought risk), where grid cells with high drought risk and high species richness have higher species-assemblage risk than gird cells with high drought risk and low species richness (low species-assemblage risk).

# Intensity
names(PDSI_2C_diff_rast) <- "delta_int_2C"
names(PDSI_4C_diff_rast) <- "delta_int_4C"

# Frequency
freq_diff_rast <- rasterFromXYZ(freq_4C_diff_df) # convert to raster
crs(freq_diff_rast) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
freq_diff_rast <- projectRaster(freq_diff_rast, anuran_sr) # match anuran extent
freq_diff_df   <- raster::as.data.frame(raster::rasterToPoints(freq_diff_rast))

PDSI_freq_diff <- subset(freq_diff_rast, 4:5)
names(PDSI_freq_diff) <- c("delta_freq_2C", "delta_freq_4C")

# Duration
dur_diff_rast <- raster::rasterFromXYZ(dur_4C_df) # convert to raster
crs(dur_diff_rast) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
dur_diff_rast <- raster::projectRaster(dur_diff_rast, anuran_sr) # match anuran extent
dur_diff_df <- raster::as.data.frame(raster::rasterToPoints(dur_diff_rast))

PDSI_dur_diff  <- subset(dur_diff_rast, 4:5)
names(PDSI_dur_diff) <- c("delta_dur_2C", "delta_dur_4C")

# Combine relative PDSI metrics
PDSI_risk_comb_rast <- raster::stack(PDSI_2C_diff_rast, PDSI_4C_diff_rast, PDSI_freq_diff, PDSI_dur_diff, resample(anuran_sr, PDSI_4C_diff_rast))
                             
PDSI_risk_comb_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_risk_comb_rast)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(delta_int_2C_bin = ifelse(delta_int_2C < -1, 1, 0), # delta_PDSI < -1
         delta_int_4C_bin = ifelse(delta_int_4C < -1, 1, 0), # delta_PDSI < -1
         delta_freq_2C_bin = ifelse(delta_freq_2C >1, 1, 0), # month > 1
         delta_freq_4C_bin = ifelse(delta_freq_4C >1, 1, 0), # month > 1
         delta_dur_2C_bin = ifelse(delta_dur_2C >1, 1, 0), # month > 1
         delta_dur_4C_bin = ifelse(delta_dur_4C >1, 1, 0)) %>% # month > 1 
  dplyr::rowwise() %>%
  dplyr::mutate(count_2C = sum(delta_int_2C_bin, delta_freq_2C_bin, delta_dur_2C_bin, na.rm = T),
         
         count_4C = sum(delta_int_4C_bin, delta_freq_4C_bin, delta_dur_4C_bin, na.rm = T),
         risk_2C  = sp_n * count_2C,
         risk_4C  = sp_n * count_4C,
         count_2C = factor(count_2C, levels = c("3", "2", "1")),
         count_4C = factor(count_4C, levels = c("3", "2", "1"))
         )
# Combine absolute PDSI metrics
PDSI_ab_rast <- raster::stack(PDSI_cur_mean_rast, PDSI_2C_mean_rast, PDSI_4C_mean_rast,
    freq_diff_rast, dur_diff_rast, resample(anuran_sr, PDSI_4C_mean_rast))
PDSI_ab_rast_crop <- raster::mask(crop(PDSI_ab_rast, extent(world)), world)  # crop 

PDSI_ab_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ab_rast_crop)) %>%
    dplyr::rename(PDSI_cur = "layer.1", PDSI_2C = "layer.2", PDSI_4C = "layer.3",
        freq_cur = "mean_curr.1", freq_2C = "mean_2C.1", freq_4C = "mean_4C.1", dur_cur = "mean_curr.2",
        dur_2C = "mean_2C.2", dur_4C = "mean_4C.2", sp_n = "layer.4") %>%
    filter(sp_n != "NA")

colours_PDSI <- RColorBrewer::brewer.pal(9, "RdBu")

# Intensity
PDSI_cur_plot <- PDSI_ab_df %>%
    dplyr::mutate(PDSI_cur_cat = case_when(PDSI_cur >= 2 & PDSI_cur < 3 ~ "2", PDSI_cur >=
        1 & PDSI_cur < 2 ~ "1", PDSI_cur >= -1 & PDSI_cur < 1 ~ "0")) %>%
    dplyr::mutate(PDSI_cur_cat = factor(PDSI_cur_cat, levels = c("0", "1", "2"),
        ordered = TRUE)) %>%
    filter(PDSI_cur_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = PDSI_cur_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#F7F7F7", "#D1E5F0", "#92C5DE")) + theme_void() +
    ylab(NULL) + xlab(NULL) + ggtitle("Mean PDSI intensity (1970–2000)") + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
    plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

PDSI_2C_plot <- PDSI_ab_df %>%
    dplyr::mutate(PDSI_2C_cat = case_when(PDSI_2C >= 4 ~ "4", PDSI_2C >= 3 & PDSI_2C <
        4 ~ "3", PDSI_2C >= 2 & PDSI_2C < 3 ~ "2", PDSI_2C >= 1 & PDSI_2C < 2 ~ "1",
        PDSI_2C >= -1 & PDSI_2C < 1 ~ "0", PDSI_2C >= -2 & PDSI_2C < -1 ~ "-1", PDSI_2C >=
            -3 & PDSI_2C < -2 ~ "-2", PDSI_2C >= -4 & PDSI_2C < -3 ~ "-3", PDSI_2C <
            -4 ~ "-4")) %>%
    dplyr::mutate(PDSI_2C_cat = factor(PDSI_2C_cat, levels = c("-4", "-3", "-2",
        "-1", "0", "1", "2", "3", "4"), ordered = TRUE)) %>%
    filter(PDSI_2C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = PDSI_2C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = colours_PDSI) + theme_void() + ylab(NULL) + xlab(NULL) +
    ggtitle("Mean PDSI intensity (+2°C)") + scale_y_continuous(limits = c(-60, 90),
    expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
    theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

PDSI_4C_plot <- PDSI_ab_df %>%
    dplyr::mutate(PDSI_4C_cat = case_when(PDSI_4C >= 4 ~ "4", PDSI_4C >= 3 & PDSI_4C <
        4 ~ "3", PDSI_4C >= 2 & PDSI_4C < 3 ~ "2", PDSI_4C >= 1 & PDSI_4C < 2 ~ "1",
        PDSI_4C >= -1 & PDSI_4C < 1 ~ "0", PDSI_4C >= -2 & PDSI_4C < -1 ~ "-1", PDSI_4C >=
            -3 & PDSI_4C < -2 ~ "-2", PDSI_4C >= -4 & PDSI_4C < -3 ~ "-3", PDSI_4C <
            -4 ~ "-4")) %>%
    dplyr::mutate(PDSI_4C_cat = factor(PDSI_4C_cat, levels = c("-4", "-3", "-2",
        "-1", "0", "1", "2", "3", "4"), ordered = TRUE)) %>%
    filter(PDSI_4C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = PDSI_4C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = colours_PDSI, name = "PDSI", guide = guide_legend(reverse = TRUE,
        nrow = 1)) + theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Mean PDSI intensity (+4°C)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10), legend.title = element_text(size = 8),
    legend.key.height = unit(0.2, "cm"), legend.key.width = unit(0.2, "cm"), legend.position = "bottom") +
    coord_fixed(ratio = 1)  # fixed ratio

int_legend <- cowplot::get_legend(PDSI_4C_plot)

int_prow <- cowplot::plot_grid(PDSI_cur_plot + theme(legend.position = "none"), PDSI_2C_plot +
    theme(legend.position = "none"), PDSI_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("a", "b", "c"))

int_plots <- cowplot::plot_grid(int_prow, int_legend, ncol = 1, rel_heights = c(1,
    0.1))

# Frequency
freq_cur_plot <- PDSI_ab_df %>%
    dplyr::mutate(freq_cur_cat = case_when(freq_cur > 0.1 & freq_cur < 1 ~ "<1")) %>%
    filter(freq_cur_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = freq_cur_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = "#ededed") + theme_void() + ylab(NULL) + xlab(NULL) +
    ggtitle("Mean drought frequency (1970–2000)") + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
    plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

freq_2C_plot <- PDSI_ab_df %>%
    dplyr::mutate(freq_2C_cat = case_when(freq_2C >= 10 ~ "10-12", freq_2C >= 8 &
        freq_2C < 10 ~ "8-10", freq_2C >= 6 & freq_2C < 8 ~ "6-8", freq_2C >= 4 &
        freq_2C < 6 ~ "4-6", freq_2C >= 2 & freq_2C < 4 ~ "2-4", freq_2C >= 1 & freq_2C <
        2 ~ "1-2", freq_2C > 0.1 & freq_2C < 1 ~ "<1")) %>%
    dplyr::mutate(freq_2C_cat = factor(freq_2C_cat, levels = c("10-12", "8-10", "6-8",
        "4-6", "2-4", "1-2", "<1"), ordered = TRUE)) %>%
    filter(freq_2C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = freq_2C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7")) + theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Mean drought frequency (+2°C)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

freq_4C_plot <- PDSI_ab_df %>%
    dplyr::mutate(freq_4C_cat = case_when(freq_4C >= 10 ~ "10-12", freq_4C >= 8 &
        freq_4C < 10 ~ "8-10", freq_4C >= 6 & freq_4C < 8 ~ "6-8", freq_4C >= 4 &
        freq_4C < 6 ~ "4-6", freq_4C >= 2 & freq_4C < 4 ~ "2-4", freq_4C >= 1 & freq_4C <
        2 ~ "1-2", freq_4C > 0.1 & freq_4C < 1 ~ "<1")) %>%
    dplyr::mutate(freq_4C_cat = factor(freq_4C_cat, levels = c("10-12", "8-10", "6-8",
        "4-6", "2-4", "1-2", "<1"), ordered = TRUE)) %>%
    filter(freq_4C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = freq_4C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7"), name = "Months", guide = guide_legend(reverse = TRUE,
        nrow = 1)) + theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Mean drought frequency (+4°C)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10), legend.title = element_text(size = 8),
    legend.key.height = unit(0.2, "cm"), legend.key.width = unit(0.2, "cm"), legend.position = "bottom") +
    coord_fixed(ratio = 1)  # fixed ratio

freq_legend <- cowplot::get_legend(freq_4C_plot)

freq_prow <- cowplot::plot_grid(freq_cur_plot + theme(legend.position = "none"),
    freq_2C_plot + theme(legend.position = "none"), freq_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("d", "e", "f"))

freq_plots <- cowplot::plot_grid(freq_prow, freq_legend, ncol = 1, rel_heights = c(1,
    0.1))


# Duration
dur_cur_plot <- PDSI_ab_df %>%
    dplyr::mutate(dur_cur_cat = case_when(dur_cur >= 2 & dur_cur < 4 ~ "2-4", dur_cur >=
        1 & dur_cur < 2 ~ "1-2", dur_cur > 0.1 & dur_cur < 1 ~ "<1")) %>%
    dplyr::mutate(dur_cur_cat = factor(dur_cur_cat, levels = c("2-4", "1-2", "<1"),
        ordered = TRUE)) %>%
    filter(dur_cur_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = dur_cur_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#FDDBC7", "#FAE9DF", "#F7F7F7")) + theme_void() +
    ylab(NULL) + xlab(NULL) + ggtitle("Mean drought duration (1970–2000)") + scale_y_continuous(limits = c(-60,
    90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180, 180), expand = c(0,
    0)) + theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(),
    plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

dur_2C_plot <- PDSI_ab_df %>%
    dplyr::mutate(dur_2C_cat = case_when(dur_2C >= 10 ~ ">10", dur_2C >= 8 & dur_2C <
        10 ~ "8-10", dur_2C >= 6 & dur_2C < 8 ~ "6-8", dur_2C >= 4 & dur_2C < 6 ~
        "4-6", dur_2C >= 2 & dur_2C < 4 ~ "2-4", dur_2C >= 1 & dur_2C < 2 ~ "1-2",
        dur_2C > 0.1 & dur_2C < 1 ~ "<1")) %>%
    dplyr::mutate(dur_2C_cat = factor(dur_2C_cat, levels = c(">10", "8-10", "6-8",
        "4-6", "2-4", "1-2", "<1"), ordered = TRUE)) %>%
    filter(dur_2C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = dur_2C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7")) + theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Mean drought duration (+2°C)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

dur_4C_plot <- PDSI_ab_df %>%
    dplyr::mutate(dur_4C_cat = case_when(dur_4C >= 10 ~ ">10", dur_4C >= 8 & dur_4C <
        10 ~ "8-10", dur_4C >= 6 & dur_4C < 8 ~ "6-8", dur_4C >= 4 & dur_4C < 6 ~
        "4-6", dur_4C >= 2 & dur_4C < 4 ~ "2-4", dur_4C >= 1 & dur_4C < 4 ~ "1-2",
        dur_4C > 0.1 & dur_4C < 1 ~ "<1")) %>%
    dplyr::mutate(dur_4C_cat = factor(dur_4C_cat, levels = c(">10", "8-10", "6-8",
        "4-6", "2-4", "1-2", "<1"), ordered = TRUE)) %>%
    filter(dur_4C_cat != "NA") %>%
    ggplot() + geom_raster(aes(y = y, x = x, fill = dur_4C_cat)) + geom_polygon(data = world_spdf,
    aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7"), name = "Months", guide = guide_legend(reverse = TRUE,
        nrow = 1)) + theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Mean drought duration (+4°C)") +
    scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) + scale_x_continuous(limits = c(-180,
    180), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10), legend.title = element_text(size = 8),
    legend.key.size = unit(0.2, "cm"), legend.position = "bottom") + coord_fixed(ratio = 1)  # fixed ratio

dur_legend <- cowplot::get_legend(dur_4C_plot)

dur_prow <- cowplot::plot_grid(dur_cur_plot + theme(legend.position = "none"), dur_2C_plot +
    theme(legend.position = "none"), dur_4C_plot + theme(legend.position = "none"),
    align = "v", ncol = 3, labels = c("g", "h", "i"))

dur_plots <- cowplot::plot_grid(dur_prow, dur_legend, ncol = 1, rel_heights = c(1,
    0.1))


cowplot::plot_grid(int_plots, freq_plots, dur_plots, ncol = 1)

Fig. S2. Mean monthly Palmer Drought Severity Index (PDSI) self-calibrated with Penman–Monteith potential evapotranspiration from (a) 1970–2000, and from 2080-2100 under (b) under an intermediate emission scenario (Shared Socioeconomic Pathways 2 - 4.5; SSP2-4.5), and (c) under a high emission scenario (SSP5-8.5). Mean yearly frequency of moderate to extreme drought (PDSI < -2) from (d) 1970–2000, and from 2080-2100 (e) under an intermediate emission scenario (SSP2-4.5), and (f) under a high emission scenario (SSP5-8.5). Mean duration of moderate to extreme drought (PDSI < -2) from (g) 1970–2000, and from 2080-2100 (h) under an intermediate emission scenario (SSP2-4.5), and (i) under a high emission scenario (SSP5-8.5).

Summary

Ecotype AI distribution

Mean ± s.d. aridity index occupied by different anuran ecotypes.

Ecotype AI (mean ± s.d.)
Stream-dwelling 1.46 ± 0.62
Arboreal 1.01 ± 0.62
Semi-aquatic 1.01 ± 0.58
Aquatic 0.98 ± 0.61
Ground-dwelling 0.89 ± 0.64
Fossorial 0.80 ± 0.61

2°C AI risk

Percent change in aridity by 2080–2100 (+ 2°C) relative to current climate (1981–2010) for all anurans and by ecotype.

Ecotype All (%) Aquatic 2°C (%) Arboreal 2°C (%) Fossorial 2°C(%) Ground-dwelling 2°C (%) Semi-aquatic 2°C (%) Stream-dwelling 2°C (%)
Hyper-arid -2.3 13.1 -15.2 -37.25 -2.0 4.7 -18.2
Arid 5.0 10.2 6.3 9.90 4.9 10.0 26.8
Semi-arid 1.4 2.1 1.5 0.75 1.6 1.1 13.9
Dry sub-humid 2.5 11.1 9.5 -3.67 8.4 2.8 10.4
Humid -1.4 -3.2 -2.2 -2.00 -2.4 -1.3 -1.3

4°C AI risk

Percent change in aridity by 2080–2100 (+ 4°C) relative to current climate (1981–2010) for all anurans and by ecotype.

Ecotype All (%) Aquatic 2°C (%) Arboreal 2°C (%) Fossorial 2°C(%) Ground-dwelling 2°C (%) Semi-aquatic 2°C (%) Stream-dwelling 2°C (%)
Hyper-arid 3.0 28.3 -4.3 -13.7 3.1 11.6 -9.1
Arid 13.2 33.0 20.6 23.1 13.5 27.0 43.7
Semi-arid 8.4 11.6 14.3 4.5 8.8 10.8 53.8
Dry sub-humid 9.0 25.2 16.6 -3.5 16.0 11.6 23.9
Humid -5.8 -9.8 -7.6 -7.5 -7.6 -5.7 -3.8

2°C PDSI risk

Percent change in drought intensity (ΔPDSI), frequency (ΔFrequency), and duration (ΔDuration) by 2080–2099 (+ 2°C scenario) relative to current climate (1970–2000) in grid cells occupied by anurans and by ecotype.

ΔPDSI All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
-4 0.01 0.00 0.00 0.00 0.00 0.01 0.00
-3 0.15 0.00 0.00 0.00 0.06 0.19 0.00
-2 2.00 4.22 3.35 4.35 2.18 2.46 5.48
-1 18.55 33.52 28.74 27.06 20.99 21.74 26.88
0 71.05 54.90 55.68 54.07 68.60 66.72 60.07
1 6.40 5.19 8.86 9.94 6.05 6.84 6.19
2 1.59 1.83 2.90 3.99 1.84 1.74 1.23
3 0.25 0.34 0.48 0.59 0.29 0.30 0.14
ΔFrequency (months) All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
10-12 0.14 0.00 0.00 0.00 0.05 0.17 0.00
8-10 0.22 0.03 0.06 0.03 0.18 0.25 0.20
6-8 0.61 0.78 0.70 0.79 0.60 0.70 0.86
4-6 2.15 3.86 3.40 3.01 2.33 2.26 3.71
2-4 12.25 21.14 19.23 16.82 13.42 13.75 26.57
1-2 16.90 24.28 17.56 18.39 18.51 18.26 10.49
0-1 56.26 42.56 46.21 46.64 53.17 54.34 48.46
-0-1 11.45 7.36 12.84 14.32 11.73 10.27 9.72
ΔDuration (months) All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
>10 0.27 0.00 0.00 0.00 0.10 0.34 0.00
8-10 0.06 0.00 0.00 0.00 0.04 0.06 0.01
6-8 0.11 0.00 0.00 0.00 0.09 0.11 0.07
4-6 0.47 0.67 0.61 0.75 0.48 0.55 0.57
2-4 7.79 14.23 11.94 10.29 8.61 9.21 15.78
1-2 38.11 34.96 29.11 23.85 38.14 39.87 27.17
0-1 39.57 41.03 42.90 47.79 38.34 37.31 44.53
-0-1 12.98 8.46 14.49 16.04 13.47 11.90 11.82
-1-2 0.64 0.65 0.95 1.28 0.71 0.64 0.04

4°C PDSI risk

Percent change in drought intensity (ΔPDSI), frequency (ΔFrequency), and duration (ΔDuration) by 2080–2099 (+ 4°C scenario) relative to current climate (1970–2000) in grid cells occupied by anurans and by ecotype.

ΔPDSI All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
-4 0.83 1.3 0.96 1.3 0.74 1.0 1.9
-3 4.44 9.9 7.80 9.6 5.04 5.4 15.2
-2 10.88 17.7 16.15 11.0 12.26 12.6 14.4
-1 22.33 26.6 19.04 25.3 24.78 23.6 12.2
0 44.62 31.9 37.41 32.8 42.33 40.2 41.6
1 9.63 4.5 6.79 5.6 6.97 9.5 6.8
2 2.94 2.2 4.17 4.3 2.87 2.9 3.4
3 1.39 1.9 2.30 2.4 1.60 1.4 1.9
4 2.94 3.9 5.38 7.7 3.41 3.4 2.5
ΔFrequency (months) All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
10-12 1.6 2.2 1.9 2.0 1.6 1.9 2.9
8-10 4.9 9.3 7.7 7.3 5.4 5.3 11.6
6-8 7.0 10.9 10.0 8.4 7.8 7.5 11.9
4-6 14.0 17.1 13.2 9.9 15.7 15.4 10.7
2-4 22.8 22.8 17.9 25.1 24.1 22.9 12.4
1-2 12.1 11.2 12.9 12.1 11.4 11.4 16.2
0-1 30.1 20.3 26.5 22.8 25.6 28.4 29.4
-0-1 7.6 6.2 9.8 12.3 8.4 7.2 4.8
ΔDuration (months) All (%) Aquatic (%) Arboreal (%) Fossorial (%) Ground-dwelling (%) Semi-aquatic (%) Stream-dwelling (%)
>10 2.53 2.8 2.6 2.6 2.46 2.84 3.90
8-10 1.02 2.0 1.6 1.6 1.14 1.14 2.16
6-8 2.33 4.0 3.7 3.1 2.60 2.50 4.47
4-6 11.07 16.6 13.5 9.0 12.48 12.55 14.75
2-4 24.82 23.0 19.5 18.4 26.38 26.19 17.99
1-2 23.42 22.9 20.5 23.1 20.52 22.95 19.89
0-1 25.18 21.7 26.0 26.7 23.70 22.79 29.51
-0-1 8.83 5.8 11.0 13.5 9.79 8.12 7.27
-1-2 0.81 1.2 1.4 1.9 0.93 0.93 0.05

Dataset

# Load and clean raw data
raw_dat <- read.csv(file.path(data_path, "raw_data.csv")) %>%
  dplyr::select(study_ID:unit) %>%
  dplyr::mutate(ecotype  = factor(ecotype),
         family   = factor(family),
         origin   = factor(origin),
         strategy = factor(case_when(strategy == "" ~ "none", TRUE ~ as.character(strategy))),
         strategy = fct_relevel(strategy, "none", "water-proof", "cocoon", "hollow"),
         trait    = factor(trait),
         response = factor(response),
         lnMass   = log(mean_mass_g),
         lnFlow   = log(airflow_cm_s + 1),
         es_kPa   = ifelse(trait == "water loss", 0.611 * exp(2500000 / 461.5 * (1 / 273 - 1 / (trt_temp + 273.15))), NA), # saturation vapor pressure (kPa) at a given temperature
         ea_kPa   = ifelse(trait == "water loss", RH_perc * es_kPa / 100, NA), # actual vapor pressure (kPa)
         VPD_kPa  = es_kPa - ea_kPa,
         lnVPD    = log(VPD_kPa)) %>%
  dplyr::filter(species_phylo != "") # remove rows with no species

ewl_dat <- raw_dat %>%
  dplyr::filter(response == "evaporative water loss" & !is.na(unit_corrected_mean)) %>%
  dplyr::mutate(mg_h_mean = unit_corrected_mean * dors_SA_cm2,
                mg_h_sd   = unit_corrected_sd * dors_SA_cm2,    
                lnMean    = log(mg_h_mean),
                v         = mg_h_sd^2 / sample_size, # sampling variance (v)
                sei       = sqrt(v), # standard error (SE)
                inv       = 1 / sei, # precision (inverse of SE) )
                w         = 1 / v, # weight (inverse of variance) 
                h_70      = (mean_mass_g - (mean_mass_g * 0.7)) / (mg_h_mean * 0.001)) 

resist_dat <- raw_dat %>%
  dplyr::filter(unit == "s cm" & !is.na(unit_corrected_mean))

wu_dat <- raw_dat %>%
  dplyr::filter(response == "water uptake" & !is.na(unit_corrected_mean)) %>%
  dplyr::mutate(mg_h_mean = unit_corrected_mean * vent_SA_cm2,
                mg_h_sd   = unit_corrected_sd * vent_SA_cm2,
                lnMean    = log(mg_h_mean),
                v         = mg_h_sd^2 / sample_size, # sampling variance (v)
                sei       = sqrt(v), # standard error (SE)
                inv       = 1 / sei) # precision (inverse of SE)

#ewl_dat %>%
  #group_by(strategy) %>%
  #summarise(mean = mean(h_70, na.rm = TRUE))

There are 238 species with data on evaporative water loss (102 studies), 145 species with skin resistance data (43 studies), and 121 species with water uptake data (51 studies). We used the evaporative water loss to analyse differences between ecotype as there are more species represented compared to the skin resistance data. There were three species that were excluded from the analysis because they were not listed in the IUCN Red List and were not present in the phylogenetic tree from Jetz and Pyron (2018): Brachycephalus pitanga, Elachistocleis cesarii, and Leptodactylus luctator.

Geographical bias in hydroregulation studies

Fig. S4. Spatial distribution of hydroregulation studies using wild caught anurans (study n = 113). 19 studies used captive raised anurans. There are large regions around central Africa and Eurasia with high amphibian diversity, but no hydrological studies conducted (Fig. 1b).

Prepare phylogeny for analysis

The phylogeny was obtained from Jetz and Pyron (2018) comprising of 7,238 species. The tree was pruned to match the species extracted for the subsequent analysis.

# Load tree
phylo_tree <- ape::read.tree(file.path(data_path, "amph_shl_new_Consensus_7238.tre"))

# Pruning data and phylogeny
tree_tip_label <- phylo_tree$tip.label  # extract tree tip names
sp_list <- raw_dat$species_phylo  # extract species name from mean data
pruned_tree <- ape::drop.tip(phylo_tree, setdiff(phylo_tree$tip.label, sp_list))  # prune phylo_tree to keep species from the raw data
pruned_tree <- phytools::force.ultrametric(pruned_tree, method = "extend")  # ultrametricize the tree
phylo_cor <- vcv(pruned_tree, cor = T)

setdiff(raw_dat$species_phylo, rownames(phylo_cor))
setdiff(rownames(phylo_cor), raw_dat$species_phylo)

# ape::is.ultrametric(pruned_tree)

Analysis

The original model incorporated origin (lab-raised or wild-caught) and whether the bladder was void of urine prior to the experiment. However, the model’s bulk effective samples size (ESS) was too low, indicating posterior means and medians may be unreliable. Since origin and whether the bladder was emptied did not influence EWL, they were excluded in the final model. For transparency, on average, wild-caught anurans had, on average, lower EWL relative to lab-raised anurans (-0.08 [-0.47:0.31]), and anurans with their bladder voided of urine had lower EWL (-0.17 [-0.59: 0.25]). However there is substantial variability between the wild-caught and lab-raised groups, and whether anurans with their bladder voided or not.

# options(brms.backend = 'cmdstanr') # Error from using Rstans 'error in
# unserialize(socklist[[n]]) : error reading from connection'. Used cmdstanr
# around it.

priors <- c(prior(normal(0, 3), "b"), prior(normal(0, 3), "Intercept"), prior(student_t(3,
    0, 10), "sd"), prior(student_t(3, 0, 10), "sigma"))

ewl_model <- brms::brm(lnMean ~ ecotype + strategy + lnMass + lnVPD + lnFlow + (1 |
    study_ID) + (1 + lnMass | species_iucn) + (1 | gr(species_phylo, cov = phylo)),
    data = ewl_dat, family = gaussian(), data2 = list(phylo = phylo_cor), prior = priors,
    chains = 4, cores = 4, iter = 5000, warmup = 2500, control = list(adapt_delta = 0.99,
        max_treedepth = 15))

wu_model <- brms::brm(lnMean ~ ecotype + lnMass + trt_temp + hydration + origin +
    (1 | study_ID) + (1 + lnMass | species_iucn) + (1 | gr(species_phylo, cov = phylo)),
    data = wu_dat %>%
        dplyr::group_by(ecotype) %>%
        dplyr::filter(n() >= 5), family = gaussian(), data2 = list(phylo = phylo_cor),
    prior = priors, chains = 4, cores = 4, iter = 5000, warmup = 2500, control = list(adapt_delta = 0.99,
        max_treedepth = 15))

Fig. S5. Scatterplots of the observed data (y) vs the average simulated data (yrep) from the posterior predictive distribution for the (a) evaporative water loss model, and (b) the water uptake model. Dashed line represents a slope of 1.

Model output

Table S3 - EWL model

Table S3. Mean parameter estimates, estimate error, and 95% Bayesian credible intervals for the evaporative water loss model, which includes the intercept (\(\beta_0\)), ecotype, strategy, the natural logarithm of body mass (lnMass), the natural logarithm of vapour pressure deficit (lnVPD), and the natural logarithm of the experimental flow rate (lnFlow). Group-level effects include the standard deviations (\(\sigma\)) for study-level observations (\(\sigma_{study}^2\)), phylogenetic relatedness (\(\sigma_{phylogeny}^2\)), and the correlation among species (\(\sigma_{species}^2\)) and body mass (\(\sigma_{lnMass}^2\)). \(R_{marginal}^2\) represents the variance explained by fixed effects, while \(R_{conditional}^2\) represents the variance explained by both fixed effects and group-level effects.

Parameter Estimate Est.Error Q2.5 Q97.5
Fixed effects
ln\(\beta_0\) 3.76 0.52 2.71 4.79
Arboreal -0.40 0.53 -1.45 0.62
Fossorial -0.01 0.53 -1.06 1.00
Ground-dwelling -0.08 0.52 -1.12 0.94
Semi-aquatic 0.06 0.52 -0.99 1.06
Stream-dwelling 0.20 0.55 -0.90 1.28
Water-proof -1.77 0.16 -2.08 -1.46
Cocoon -1.83 0.13 -2.09 -1.58
Hollow -1.99 0.35 -2.68 -1.29
lnMass 0.55 0.03 0.49 0.61
lnVPD 0.29 0.05 0.19 0.39
lnFlow 0.33 0.06 0.21 0.46
Group-level effects
\(\sigma_{species}^2\) 0.10 0.07 0.00 0.26
\(\sigma_{lnMass}^2\) 0.07 0.03 0.01 0.13
cor(\(\sigma_{species}^2\), \(\sigma_{lnMass}^2\)) -0.09 0.57 -0.95 0.94
\(\sigma_{phylogeny}^2\) 0.39 0.08 0.23 0.55
\(\sigma_{study}^2\) 0.92 0.08 0.78 1.10
Phylogenetic signal
\(\lambda\) 0.42 0.11 0.19 0.61
Variance
\(R_{marginal}^2\) 64.07 60.14 67.39
\(R_{conditional}^2\) 91.97 90.92 92.87

Table S4 - WU model

Table S4. Mean parameter estimates, estimate error, and 95% Bayesian credible intervals for the water uptake model, which includes the intercept (\(\beta_0\)), ecotype, the natural logarithm of body mass (lnMass), treatment temperature, initial hydration level, and origin (lab-raised or wild caught). Group-level effects include the standard deviations (\(\sigma\)) for study-level observations (\(\sigma_{study}^2\)), phylogenetic relatedness (\(\sigma_{phylogeny}^2\)), and the correlation among species (\(\sigma_{species}^2\)) and body mass (\(\sigma_{lnMass}^2\)). \(R_{marginal}^2\) represents the variance explained by fixed effects, while \(R_{conditional}^2\) represents the variance explained by both fixed effects and group-level effects.

Parameter Estimate Est.Error Q2.5 Q97.5
Fixed effects
ln\(\beta_0\) 8.89 0.85 7.24 10.54
Fossorial -0.42 0.29 -1.00 0.14
Ground-dwelling -0.18 0.22 -0.62 0.24
Semi-aquatic -0.16 0.29 -0.71 0.42
ecotypeStream-dwelling -0.62 0.31 -1.22 -0.02
lnMass 0.80 0.05 0.70 0.90
Treatment temperature 0.01 0.01 -0.01 0.04
Initial hydration -0.05 0.01 -0.07 -0.04
Origin - Wild 0.03 0.43 -0.80 0.88
Group-level effects
\(\sigma_{species}^2\) 0.24 0.14 0.01 0.52
\(\sigma_{lnMass}^2\) 0.09 0.05 0.01 0.20
cor(\(\sigma_{species}^2\), \(\sigma_{lnMass}^2\)) 0.10 0.54 -0.89 0.96
\(\sigma_{phylogeny}^2\) 0.58 0.19 0.18 0.95
\(\sigma_{study}^2\) 1.03 0.13 0.80 1.32
Phylogenetic signal
\(\lambda\) 0.77 0.16 0.28 0.94
Variance
\(R_{marginal}^2\) 69.80 62.86 74.97
\(R_{conditional}^2\) 97.72 96.55 98.60

Fig. S6 - EWL model

Fig. S6. Differences in evaporative water loss (mg H2O h-1) by (a) ecotype and (b) water-conserving strategies. Note, when plotting by ecotype, the evaporative loss excludes behavioural water-conserving strategies such as during cocoon-forming and inside hollows. Mean estimates ± 95% CI presented in black points and error bars, while raw values were presented as grey points. The size of the grey points indicates study precision (inverse of standard error).

Fig. S7 - WU model

Fig. S7. Differences in cutaneous water uptake (mg H2O h-1) by ecotype. Mean estimates ± 95% CI presented in black points and error bars, while raw values were presented as grey points. The size of the grey points indicates study precision (inverse of standard error).

NicheMapR

To estimate the influence of warming and drought on activity of a hypothetical frog, we simulated a water and heat energy exchange model (Fig. S8a) and its interaction with a simulated local microclimate using the NicheMapR package (Kearney and Porter, 2017; Kearney and Porter, 2020).

Fig. S8. Summary water and energy exchange model from Tracy (1976) integrated into NicheMapR and water conserving strategies. (a) Schematic summary of the exchanges of energy and water between a frog and its environment used to develop the transient-state model of water exchange. In respect to water exchange, the net water loss represents water loss from respiratory, cutaneous, ocular, and cloaca evaporation as well as urinary and faecal water (Pirtle et al., 2019). (b) Behavioural, morphological, and physiological strategies employed by frogs on land to reduce water loss (Hillman et al., 2009).

Simulate water-conserving behaviours

We modified the current ectoR_devel function from NicheMapR v3.2.1 to account for the influence of hydration on activity (behav_functions function) which can be found in the behav_functions.R file on GitHub.

Ask Urtzi to elaborate and modify this section what was changed and what it represents

When the animal is not active (i.e. is below ground), it is simulated to go to an underground retreat. It selects the shallowest depth with temperatures between Tmax and Tmin. If water = T, the frog selects the shallowest node with temperatures between Tmax and Tmin and a water potential >= -72.5, which was reported as a soil water potential from which Rana pipiens could absorb water. When the animal is belowground, it re-hydrates at a rate specified in hyd.rate, if the soil water potential is >= -72.5. Important to note that currently frogs are not re-hydrating when active aboveground.

The “skinwet” term (\(p_{wet}\)) determines the proportion of the total surface area used in the calculation of mass transfer of water from the surface. Here, \(p_{wet}\) was calculated from empirically derived skin resistance following Pirtle et al. (2019):

\[\begin{equation} p_{wet} = \frac{1}{h_D \times r_i + (\frac{P_r}{S_c})^\frac{2}{3}}, \tag{16} \end{equation}\]

where \(r_i\) is the skin’s resistance to water vapor transfer (s m-1), \(P_r\) is the Prandtl number (dimensionless), \(S_c\) is the Schmidt number (dimensionless), \(h_D\) is the mass transfer coefficient (m s-1). To calculate \(p_{wet}\), the mass transfer coefficient (\(h_D\)) must be known. Mass transfer refers to the movement of a substance though a fluid interface, driven by changes in the concentration gradient. The mass transfer coefficient controls the rate of diffusion and is dependent on velocity, temperature, and physical properties of the interphase. Similar to the heat transfer coefficient, the mass transfer coefficient also has two components derived from free and forced convection. We consider forced convection only (as free convention should be negligible within a measurement chamber), and this can be calculated from the heat transfer coefficient:

\[\begin{equation} h_D = (\frac{h_C}{C_p \times \rho}) \times (\frac{P_r}{S_c}) ^ \frac{2}{3}, \tag{17} \end{equation}\]

where \(P_r\) is the Prandtl number (dimensionless), \(S_c\) is the Schmidt number (dimensionless), \(h_C\) is the heat transfer coefficient (J s-1 cm-2 K-1), \(\rho\) is the density of dry air (g cm-3), and \(C_p\) is the specific heat of air (1.01 J g-1 K-1).

Simulate drought and warming

Microclimates represent the physical environments experienced by an organism. They are a necessity for mechanistic niche modelling because it is the environment experienced at the scale of the individual that needs to be provided to the equations of energy and mass balance. The microclimate model was implemented as described by Kearney et al. (2014). Specifically, it was driven by historical 0.05° grid (~5 km) daily weather input layers (air temperature, vapor pressure, wind speed, and cloud cover).

We simulated meteorological drought (defined as less than average rainfall) by modifying the micro_era5 function from NicheMapR to incorporate rainfall (rainfact) and relative humidity (rhfact) deficit factors. A default factor is 1 which is 100% of the original rainfall or relative humidity. A factor of 0.5 means 50% of the original rainfall or relative humidity. The modified function can be found on the accompanying GitHub page.

To simulate frog activity under drought and warming scenarios, we constructed four climate conditions using the micro_era5 function:

  1. current normal scenario representing the mean annual air temperature and rainfall from 1981–2010,
  2. current drought scenario representing the mean annual air temperature from 1981-2010 and annual rainfall based on 2017-2019 drought in Australia (Fig. S9),
  3. warming normal scenario representing the “business-as-usual” scenario, where the global surface temperature is estimated to increase by 4°C by 2080–2100 with no effect of drought (+4°C only), and
  4. warming drought scenario with a +4°C increasing in air temperature and annual rainfall based on 2017-2019 drought in Australia.

First, download the mcera5 microclimate data to a local directory to run the micro_era5 function faster.

# get ERA5 data with package mcera5 assign your credentials (register here:
# https://cds.climate.copernicus.eu/user/register)
uid <- "$$$$$$"
cds_api_key <- "$$$$$$$$-$$$$-$$$$-$$$$-$$$$$$$$$$$$"
ecmwfr::wf_set_key(user = uid, key = cds_api_key, service = "cds")

# bounding coordinates (in WGS84 / EPSG:4326)
c(-41.19, -20.186)
xmn <- -42
xmx <- -41
ymn <- -21
ymx <- -20

xmn <- 152
xmx <- 154
ymn <- -28
ymx <- -26

# temporal extent
st_time <- lubridate::ymd("2016:01:01")  # earliest sampling date
en_time <- lubridate::ymd("2018:12:31")  # latest sampling date

# filename and location for downloaded .nc files
file_prefix <- "era5"
op <- "YOUR DIRECTORY"

# build a request (covering multiple years)
req <- mcera5::build_era5_request(xmin = xmn, xmax = xmx, ymin = ymn, ymax = ymx,
    start_time = st_time, end_time = en_time, outfile_name = file_prefix)

mcera5::request_era5(request = req, uid = uid, out_path = op)

Next, run the micro_era5 function for the four scenarios in 2017 (representing typical rainfall year), and one for 2019 to validate the observed rainfall data.

source("/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/code/micro_era5_drought.R")

longlat <- c(153.09249, -27.6235) # Karawatha, QLD.

# Current normal 2017 scenario
micro_curr_wet <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 1.2, rhfact = 1, # rain and RH parameters
                             warm = 0, # current climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Current dry 2017 scenario
micro_curr_dry <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 0.5, rhfact = 0.1, # rain and RH parameters
                             warm = 0, # current climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Warming normal 2017 scenario
micro_warm_wet <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 1.2, rhfact = 1, # rain and RH parameters
                              warm = 4, # future climate
                              dstart = "01/01/2017", 
                              dfinish = "31/12/2017",
                              spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                              save = 0)

# Warming dry 2017 scenario (simulating 2019 drought)
micro_warm_dry <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 0.5, rhfact = 0.1, # rain and RH parameters
                             warm = 4, # future climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Simulate rainfall for 2019 drought
act_dry <- micro_era5(loc = longlat, 
                      runshade = 1, minshade = 0, # shade parameters
                      warm = 0, # current climate
                      dstart = "01/01/2019", 
                      dfinish = "31/12/2019",
                      spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                      save = 0)

To verify our microclimate models, we plotted the simulated the daily rainfall for 2017 (representing typical rainfall) and 2019 (historical drought) with the observed daily rainfall in Australia for the following location: Karawatha, Southeast Queensland, Australia (153.09249, -27.6235). Karawatha provides a good case study because there are many ecotypes found in this location: ground-dwelling (e.g. Rhinella marina), arboreal (e.g. Litoria caerulea), fossorial (e.g. Cyclorana alboguttata), semi-aquatic (e.g. Litoria nasuta), and this area has experienced drought recently (2019). We extracted rainfall data from a weather station next to Karawatha which experienced “very much below average” rainfall from the Australian Bureau of Meteorology.

# Load observed rainfall for 2019
obs_rainfall <- read.csv(file.path(data_path, "obs_rainfall.csv")) %>%
    tibble::rowid_to_column("DOY") %>%
    dplyr::mutate(date = lubridate::make_date(year = 2019, month = month_num, day = day))

# Merge observed and predicted rainfall together

model_rain_wet <- as.data.frame(micro_curr_wet$RAINFALL) %>%
    tibble::rowid_to_column("DOY") %>%
    rename(rainfall_mm = "micro_curr_wet$RAINFALL") %>%
    merge(obs_rainfall, by = "DOY", all.x = TRUE)

model_2019 <- as.data.frame(act_dry$RAINFALL) %>%
    tibble::rowid_to_column("DOY") %>%
    rename(rainfall_mm = "act_dry$RAINFALL") %>%
    merge(obs_rainfall, by = "DOY", all.x = TRUE)

# 2019 drought validate
rain_dry_plot <- model_2019 %>%
    ggplot() + geom_line(aes(x = DOY, y = karawatha_19), colour = "grey") + geom_line(aes(x = DOY,
    y = rainfall_mm), colour = "red", alpha = 0.5) + labs(x = NULL, y = "Daily rainfall (mm)") +
    ggtitle("2019 Drought") + scale_y_continuous(lim = c(0, 100), expand = c(0, 0)) +
    scale_x_continuous(expand = c(0, 0)) + mytheme() + theme(legend.position = "bottom")

# 2017 normal validate
rain_wet_plot <- model_rain_wet %>%
    ggplot() + geom_line(aes(x = DOY, y = karawatha_17), colour = "grey") + geom_line(aes(x = DOY,
    y = rainfall_mm), colour = "red", alpha = 0.5) + labs(x = NULL, y = "Daily rainfall (mm)") +
    ggtitle("2017 Normal") + scale_y_continuous(lim = c(0, 100), expand = c(0, 0)) +
    scale_x_continuous(expand = c(0, 0)) + mytheme() + theme(legend.position = "bottom")

cowplot::plot_grid(rain_wet_plot, rain_dry_plot, ncol = 2)

Fig. S9. Simulated rainfall from the micro_era5 function (red) and the observed rainfall for Karawatha, QLD from the Australian Bureau of Meteorology (grey). The total yearly observed rainfall for 2017 was as 1100 mm, and the total simulated yearly rainfall from NicheMapR was 1097.12 mm. The total yearly observed rainfall for the 2019 drought was 598 mm, and the total simulated yearly rainfall from NicheMapR was 556.22 mm. The average yearly rainfall across 1981-2010 is around 1100 mm per year.

Simulate activity

We simulated the potential number of hours for activity in a year (\(t_{act}\)) which represents the suitable thermal and hydric conditions for the animal to move beyond their retreat to either catch prey or finding mates (Kearney and Porter, 2020). The thermoregulatory and hydroregulatory sequence in the model assumes that frogs will only be ‘active’ (i.e. moving beyond their retreat, catching prey, finding mates) between the minimum and maximum body temperature thresholds for foraging, Tmin and Tmax (°C), and the minimum tolerated hydration, min.hyd (%). In addition, if water.act = T, the frog will only be active if temperatures are within the suitable temp range, and the frog is not expected to go below allowed hydration levels (e.g. if %hydration is not below min.hyd).

To estimate the potential \(t_{act}\) for one year, we simulated the hypothetical frog to be active during the day and night (24 hours). While most frogs are nocturnal, there are some frog species found moving during the day. The water hydric parameters (e.g. skin resistance, water uptake rate, dehydration tolerance) were based on the average values of the dataset collected from the PRISMA search. The thermal parameters (minimum and maximum foraging and critical temperature) were based on Rhinalla marina which has been verified in Kearney et al. (2008).

Three water-saving strategies were constructed to broadly reflect each ecotype that use either behavioural strategies such as microhabitat selection, or physiological strategies such as increased skin resistance or skin thickness (Table S5). For example, many amphibians (espically arid specialist) seek or burrow underground to regulate body temperature and water balance without exhibiting thicker skin or higher skin resistance. This is because underground burrows are generally cooler, less varied temperature fluctuation, and more moist relative to the surface climate.

Table S5. Modified parameters for the sim.ecto function for each hypothetical frog model. The skin resistance values were based on the average empirical measurements for a typical frog and a waterproof frog in the meta-analysis. Seek shade represents microclimate simulated under shaded conditions (0-90% shade). Retreat underground represents the ability for the frog to seek suitable microclimates underground. Can climb represents the ability for the frog to seek suitable microclimates above ground level (e.g. trees, cliffs). Note, frogs are not active when underground, and water uptake only occurs during inactivity.

Water-saving strategy Skin resistance Seek shade? Retreat underground? Can climb? Ecotype
Shade only Low Yes No No Ground-dwelling
Waterproof High Yes No Yes Arboreal
Fossorial Low Yes Yes No Fossorial

It is important to note that we focus only on the direct effects of environmental change on water budgets by predicting changes in EWL rates. The conclusions we draw do not account for the indirect effects of environmental change on water budgets, such as potential changes to thermal tolerance as a result of dehydration, or the thermoregulatory difficulties that may arise as a result of habitat modification.

source("/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/code/behav_functions.R")
# Construct frog model
# compute the heat exchange by convection (extract mass transfer coefficient, Prandtl number and Schmidt number)
CONV_out <- NicheMapR::CONV_ENDO(TS     = 19, # skin temperature (°C)
                                 TENV   = 20, # fluid temperature (°C)
                                 SHAPE  = 4, # 4 is ellipsoid
                                 SURFAR = mean(resist_dat$dors_SA_cm2, na.rm = TRUE) / 10000,  # surface area for convection, m2
                                 FLTYPE = 0, # fluid type: 0 = air
                                 FURTST = 0, # test of presence of fur (length x diameter x density x depth) (-)
                                 D      = mean(resist_dat$D, na.rm = TRUE), 
                                 TFA    = 20, # initial fur/air interface temperature
                                 VEL    = mean(resist_dat$airflow_cm_s, na.rm = TRUE) / 100, # wind speed (m/s)
                                 ZFUR   = 0, # fur depth, mean (m)
                                 BP     = 101325, # barometric pressure at sea level
                                 ELEV   = 0) # elevation (m)

# basic parameters for 30 g frog
Ww_g         <- exp(mean(log(raw_dat$mean_mass_g), na.rm = TRUE)) # geometric mean wet weight of animal (g), account for uneven distribution
r_s_low      <- resist_dat %>% dplyr::filter(strategy == "none") %>% dplyr::select(unit_corrected_mean) # skin resistance
pct_wet_high <- 1 / (CONV_out[5] * mean(r_s_low$unit_corrected_mean) + (CONV_out[11] / CONV_out[13]) ^ 0.6666666) * 100  # % of surface area acting as a free-water exchanger (Pirtle et al 2017)

# parameters for a water-proof frog
r_s_high    <- resist_dat %>% dplyr::filter(strategy == "water-proof") %>% dplyr::select(unit_corrected_mean) # skin resistance
pct_wet_low <- 1 / (CONV_out[5] * max(r_s_high$unit_corrected_mean) + (CONV_out[11] / CONV_out[13]) ^ 0.6666666) * 100 # % of surface area acting as a free-water exchanger (Pirtle et al 2017)

# Thermal traits based on Rhinella marina
Tmin   <- 13.7 # minimum Tb at which activity occurs (Kearney et al 2008)
Tmax   <- 30.4 # maximum Tb at which activity occurs (Kearney et al 2008)
#T_pref <- 24 # preferred Tb (Kearney et al 2008)
CTmax  <- 37 # critical thermal minimum (affects choice of retreat) Tracy et al 2012
CTmin  <- 8 # critical thermal maximum (affects choice of retreat) Kolbe et al 2010, McCann et al 2014

# Water balance traits
min_hyd <- 80 # minimum tolerated hydration before activity declines (% of fully hydrated animals)
hyd.death <- 50 # minimum tolerated hydration before death (% of fully hydrated animals)
wu_rate <- wu_dat %>% dplyr::filter(strategy == "none") %>% dplyr::select(mg_h_mean)
hyd_rate <- exp(mean(log(wu_rate$mg_h_mean), na.rm = TRUE)) / 1000 # geometric mean rehydration rate (g/h), account for uneven distribution
# depends on current and max hydration like this: hyd.rate * ((hyd - hyd.current) / hyd)

# behav = 'diurnal', 'nocturnal' or 'both'
# water; does the frog select depth according to water potential? (TRUE or FALSE)
# water.act; does the activity depend on water loss? (TRUE or FALSE)

# SHADE MODEL
shad_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

# WATER-PROOF MODEL
tree_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

# BURROWING MODEL
burr_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)
# SHADE MODEL
shad_curr_wet_df <- data.frame(shad_curr_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = shad_curr_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_wet = length(active[active == TRUE]))

shad_curr_dry_df <- data.frame(shad_curr_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = shad_curr_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_dry = length(active[active == TRUE]))

shad_warm_wet_df <- data.frame(shad_warm_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = shad_warm_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_wet = length(active[active == TRUE]))

shad_warm_dry_df <- data.frame(shad_warm_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = shad_warm_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_dry = length(active[active == TRUE]))

shad_model <- shad_curr_wet_df %>%
    merge(shad_curr_dry_df, by = "day") %>%
    merge(shad_warm_wet_df, by = "day") %>%
    merge(shad_warm_dry_df, by = "day") %>%
    pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
    dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet",
        "curr_dry", "curr_wet")), season = case_when(day >= 335 ~ "summer", day >=
        1 & day <= 61 ~ "summer", day >= 62 & day <= 153 ~ "autumn", day >= 154 &
        day <= 244 ~ "winter", day >= 245 & day <= 334 ~ "spring"))

# TREE MODEL
tree_curr_wet_df <- data.frame(tree_curr_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = tree_curr_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_wet = length(active[active == TRUE]))

tree_curr_dry_df <- data.frame(tree_curr_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = tree_curr_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_dry = length(active[active == TRUE]))

tree_warm_wet_df <- data.frame(tree_warm_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = tree_warm_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_wet = length(active[active == TRUE]))

tree_warm_dry_df <- data.frame(tree_warm_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = tree_warm_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_dry = length(active[active == TRUE]))

tree_model <- tree_curr_wet_df %>%
    merge(tree_curr_dry_df, by = "day") %>%
    merge(tree_warm_wet_df, by = "day") %>%
    merge(tree_warm_dry_df, by = "day") %>%
    pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
    dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet",
        "curr_dry", "curr_wet")), season = case_when(day >= 335 ~ "summer", day >=
        1 & day <= 61 ~ "summer", day >= 62 & day <= 153 ~ "autumn", day >= 154 &
        day <= 244 ~ "winter", day >= 245 & day <= 334 ~ "spring"))

# FOSSORIAL MODEL
burr_curr_wet_df <- data.frame(burr_curr_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = burr_curr_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_wet = length(active[active == TRUE]))

burr_curr_dry_df <- data.frame(burr_curr_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = burr_curr_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(curr_dry = length(active[active == TRUE]))

burr_warm_wet_df <- data.frame(burr_warm_wet_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = burr_warm_wet_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_wet = length(active[active == TRUE]))

burr_warm_dry_df <- data.frame(burr_warm_dry_mod$act) %>%
    tibble::rowid_to_column("hour") %>%
    dplyr::rename(active = burr_warm_dry_mod.act) %>%
    dplyr::mutate(day = ceiling(1:8760/24)) %>%
    dplyr::group_by(day) %>%
    dplyr::summarise(warm_dry = length(active[active == TRUE]))

burr_model <- burr_curr_wet_df %>%
    merge(burr_curr_dry_df, by = "day") %>%
    merge(burr_warm_wet_df, by = "day") %>%
    merge(burr_warm_dry_df, by = "day") %>%
    pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
    dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet",
        "curr_dry", "curr_wet")), season = case_when(day >= 335 ~ "summer", day >=
        1 & day <= 61 ~ "summer", day >= 62 & day <= 153 ~ "autumn", day >= 154 &
        day <= 244 ~ "winter", day >= 245 & day <= 334 ~ "spring"))

Model output

Table S6a - Summary

Table S6a. Total potential activity hours per year (\(t_{act}\)) for a 8.73 g frog under different warming (current or warming) and drought (normal or drought) conditions with different water-saving strategies.

data.frame(strategy = c("Shade only", "Shade only", "Shade only", "Shade only", "Waterproof",
    "Waterproof", "Waterproof", "Waterproof", "Burrowing", "Burrowing", "Burrowing",
    "Burrowing"), temp = c("current", "current", "warming", "warming"), rain = c("normal",
    "drought"), t_act_h = c(sum(shad_curr_wet_df$curr_wet), sum(shad_curr_dry_df$curr_dry),
    sum(shad_warm_wet_df$warm_wet), sum(shad_warm_dry_df$warm_dry), sum(tree_curr_wet_df$curr_wet),
    sum(tree_curr_dry_df$curr_dry), sum(tree_warm_wet_df$warm_wet), sum(tree_warm_dry_df$warm_dry),
    sum(burr_curr_wet_df$curr_wet), sum(burr_curr_dry_df$curr_dry), sum(burr_warm_wet_df$warm_wet),
    sum(burr_warm_dry_df$warm_dry)), t_act_per = c(sum(shad_curr_wet_df$curr_wet)/8760 *
    100, sum(shad_curr_dry_df$curr_dry)/8760 * 100, sum(shad_warm_wet_df$warm_wet)/8760 *
    100, sum(shad_warm_dry_df$warm_dry)/8760 * 100, sum(tree_curr_wet_df$curr_wet)/8760 *
    100, sum(tree_curr_dry_df$curr_dry)/8760 * 100, sum(tree_warm_wet_df$warm_wet)/8760 *
    100, sum(tree_warm_dry_df$warm_dry)/8760 * 100, sum(burr_curr_wet_df$curr_wet)/8760 *
    100, sum(burr_curr_dry_df$curr_dry)/8760 * 100, sum(burr_warm_wet_df$warm_wet)/8760 *
    100, sum(burr_warm_dry_df$warm_dry)/8760 * 100)) %>%
    knitr::kable(col.names = c("Water-saving strategy", "Warming simulation", "Drought simulation",
        "$t_{act}$ (h)", "$t_{act}$ (%)"))
Water-saving strategy Warming simulation Drought simulation \(t_{act}\) (h) \(t_{act}\) (%)
Shade only current normal 3972 45
Shade only current drought 3798 43
Shade only warming normal 4318 49
Shade only warming drought 4099 47
Waterproof current normal 4634 53
Waterproof current drought 4442 51
Waterproof warming normal 4831 55
Waterproof warming drought 4587 52
Burrowing current normal 4287 49
Burrowing current drought 4190 48
Burrowing warming normal 4715 54
Burrowing warming drought 4594 52

Table S6b - Relative change year

Table S6b. Change in \(t_{act}\) (%) for each water-saving strategy relative to the current normal scenario under warming only, drought only, and warming and drought combined.

data.frame(strategy = c("Shade only", "Waterproof", "Burrowing"), curr_wet = c(sum(shad_curr_wet_df$curr_wet),
    sum(tree_curr_wet_df$curr_wet), sum(burr_curr_wet_df$curr_wet)), curr_dry = c(sum(shad_curr_dry_df$curr_dry),
    sum(tree_curr_dry_df$curr_dry), sum(burr_curr_dry_df$curr_dry)), warm_wet = c(sum(shad_warm_wet_df$warm_wet),
    sum(tree_warm_wet_df$warm_wet), sum(burr_warm_wet_df$warm_wet)), warm_dry = c(sum(shad_warm_dry_df$warm_dry),
    sum(tree_warm_dry_df$warm_dry), sum(burr_warm_dry_df$warm_dry))) %>%
    dplyr::mutate(delta_warm = (warm_wet - curr_wet)/curr_wet * 100, delta_dry = (curr_dry -
        curr_wet)/curr_wet * 100, delta_warm_dry = (warm_dry - curr_wet)/curr_wet *
        100) %>%
    dplyr::select(-c(curr_wet:warm_dry)) %>%
    knitr::kable(col.names = c("Water-saving strategy", "$\\Delta$ warming only (%)",
        "$\\Delta$ drought only (%)", "$\\Delta$ warming and drought (%)"))
Water-saving strategy \(\Delta\) warming only (%) \(\Delta\) drought only (%) \(\Delta\) warming and drought (%)
Shade only 8.7 -4.4 3.2
Waterproof 4.2 -4.1 -1.0
Burrowing 10.0 -2.3 7.2

Table S6c - Relative change season

Table S6c. Change in \(t_{act}\) (%) for each water-saving strategy relative to the current normal scenario under warming only, drought only, and warming and drought combined.

# summer
shad_model_summer <- shad_model %>%
    dplyr::filter(season == "summer") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tree_model_summer <- tree_model %>%
    dplyr::filter(season == "summer") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

burr_model_summer <- burr_model %>%
    dplyr::filter(season == "summer") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tab_summer <- data.frame(strategy = c("Shade only", "Waterproof", "Burrowing"), curr_wet = c(shad_model_summer$hours[4],
    tree_model_summer$hours[4], burr_model_summer$hours[4]), curr_dry = c(shad_model_summer$hours[3],
    tree_model_summer$hours[3], burr_model_summer$hours[3]), warm_wet = c(shad_model_summer$hours[2],
    tree_model_summer$hours[2], burr_model_summer$hours[2]), warm_dry = c(shad_model_summer$hours[1],
    tree_model_summer$hours[1], burr_model_summer$hours[1])) %>%
    dplyr::mutate(delta_warm = (warm_wet - curr_wet)/curr_wet * 100, delta_dry = (curr_dry -
        curr_wet)/curr_wet * 100, delta_warm_dry = (warm_dry - curr_wet)/curr_wet *
        100) %>%
    dplyr::select(-c(curr_wet:warm_dry)) %>%
    tibble::add_row(strategy = "**Summer**", .before = 1)

# autumn
shad_model_autumn <- shad_model %>%
    dplyr::filter(season == "autumn") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tree_model_autumn <- tree_model %>%
    dplyr::filter(season == "autumn") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

burr_model_autumn <- burr_model %>%
    dplyr::filter(season == "autumn") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tab_autumn <- data.frame(strategy = c("Shade only", "Waterproof", "Burrowing"), curr_wet = c(shad_model_autumn$hours[4],
    tree_model_autumn$hours[4], burr_model_autumn$hours[4]), curr_dry = c(shad_model_autumn$hours[3],
    tree_model_autumn$hours[3], burr_model_autumn$hours[3]), warm_wet = c(shad_model_autumn$hours[2],
    tree_model_autumn$hours[2], burr_model_autumn$hours[2]), warm_dry = c(shad_model_autumn$hours[1],
    tree_model_autumn$hours[1], burr_model_autumn$hours[1])) %>%
    dplyr::mutate(delta_warm = (warm_wet - curr_wet)/curr_wet * 100, delta_dry = (curr_dry -
        curr_wet)/curr_wet * 100, delta_warm_dry = (warm_dry - curr_wet)/curr_wet *
        100) %>%
    dplyr::select(-c(curr_wet:warm_dry)) %>%
    tibble::add_row(strategy = "**Autumn**", .before = 1)

# winter
shad_model_winter <- shad_model %>%
    dplyr::filter(season == "winter") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tree_model_winter <- tree_model %>%
    dplyr::filter(season == "winter") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

burr_model_winter <- burr_model %>%
    dplyr::filter(season == "winter") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tab_winter <- data.frame(strategy = c("Shade only", "Waterproof", "Burrowing"), curr_wet = c(shad_model_winter$hours[4],
    tree_model_winter$hours[4], burr_model_winter$hours[4]), curr_dry = c(shad_model_winter$hours[3],
    tree_model_winter$hours[3], burr_model_winter$hours[3]), warm_wet = c(shad_model_winter$hours[2],
    tree_model_winter$hours[2], burr_model_winter$hours[2]), warm_dry = c(shad_model_winter$hours[1],
    tree_model_winter$hours[1], burr_model_winter$hours[1])) %>%
    dplyr::mutate(delta_warm = (warm_wet - curr_wet)/curr_wet * 100, delta_dry = (curr_dry -
        curr_wet)/curr_wet * 100, delta_warm_dry = (warm_dry - curr_wet)/curr_wet *
        100) %>%
    dplyr::select(-c(curr_wet:warm_dry)) %>%
    tibble::add_row(strategy = "**Winter**", .before = 1)

# spring
shad_model_spring <- shad_model %>%
    dplyr::filter(season == "spring") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tree_model_spring <- tree_model %>%
    dplyr::filter(season == "spring") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

burr_model_spring <- burr_model %>%
    dplyr::filter(season == "spring") %>%
    dplyr::group_by(condition) %>%
    dplyr::summarise(hours = sum(hours))

tab_spring <- data.frame(strategy = c("Shade only", "Waterproof", "Burrowing"), curr_wet = c(shad_model_spring$hours[4],
    tree_model_spring$hours[4], burr_model_spring$hours[4]), curr_dry = c(shad_model_spring$hours[3],
    tree_model_spring$hours[3], burr_model_spring$hours[3]), warm_wet = c(shad_model_spring$hours[2],
    tree_model_spring$hours[2], burr_model_spring$hours[2]), warm_dry = c(shad_model_spring$hours[1],
    tree_model_spring$hours[1], burr_model_spring$hours[1])) %>%
    dplyr::mutate(delta_warm = (warm_wet - curr_wet)/curr_wet * 100, delta_dry = (curr_dry -
        curr_wet)/curr_wet * 100, delta_warm_dry = (warm_dry - curr_wet)/curr_wet *
        100) %>%
    dplyr::select(-c(curr_wet:warm_dry)) %>%
    tibble::add_row(strategy = "**Spring**", .before = 1)

# Render table
bind_rows(tab_summer, tab_autumn, tab_winter, tab_spring) %>%
    remove_rownames() %>%
    knitr::kable(col.names = c("Water-saving strategy", "$\\Delta$ warming only (%)",
        "$\\Delta$ drought only (%)", "$\\Delta$ warming and drought (%)"))
Water-saving strategy \(\Delta\) warming only (%) \(\Delta\) drought only (%) \(\Delta\) warming and drought (%)
Summer
Shade only -8.0 -11.0 -17.85
Waterproof -8.6 -9.5 -19.08
Burrowing -4.7 -4.5 -10.13
Autumn
Shade only 5.0 -1.7 3.55
Waterproof 2.2 -2.0 0.68
Burrowing 6.0 -1.1 5.26
Winter
Shade only 72.9 -2.3 65.68
Waterproof 42.8 -1.9 36.49
Burrowing 69.8 -2.6 66.47
Spring
Shade only 1.5 -2.2 -4.34
Waterproof -1.9 -2.9 -6.55
Burrowing 4.1 -1.2 1.66

Additional figures

temp_dat <- ewl_dat %>%
    dplyr::filter(!is.na(trt_temp))

temp_dat %>%
    ggplot(aes(x = trt_temp, y = skin_temp)) + geom_point(size = 2, colour = "grey") +
    geom_abline(intercept = 0, slope = 1, linetype = "dashed", size = 0.5) + stat_smooth(method = "lm",
    formula = y ~ x + I(x^4), se = F, colour = "black") + ylim(5, 50) + xlim(5, 50) +
    labs(x = "Air temperature (°C)", y = "Skin surface temperature (°C)") + mytheme()

Fig. S11. Relationship between exposed air temperature (°C) during the experiment and the observed skin surface temperature (°C) across 238 species. The black line represents the non-linear relationship between air temperature and skin temperature where the skin temperature remains at ~35°C when exposed to air temperatures > 40°C.

Figures

Code to produce the main document figures are detailed below. Figures produced were further modified in Adobe Illustrator for publication.

Figure 1 - AI risk

# Reproject maps
rob_proj <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
world_rob <- spTransform(world_spdf, CRSobj = rob_proj)

# land
data("land", package = "tmap")
land_raster <- as(land, "Raster")  # convert from star to raster
land_df <- raster::as.data.frame(raster::rasterToPoints(land_raster))

elevation_raster <- projectRaster(land_raster$elevation, crs = rob_proj)
slope_raster <- raster::terrain(elevation_raster, opt = "slope")
aspect_raster <- raster::terrain(elevation_raster, opt = "aspect")
hill_raster <- hillShade(slope_raster, aspect_raster, 40, 270)  #setting the elevation angle (of the sun) to 40 and the direction angle of the light to 270
hill_m <- rasterToPoints(hill_raster)
hill_df <- data.frame(hill_m)
colnames(hill_df) <- c("lon", "lat", "hill")

# Fig 1a - AI current
AI_rob <- raster::projectRaster(ai_rast, crs = rob_proj)
AI_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_rob))
AI_df_rob <- AI_df_rob %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

AI_2C_rob <- projectRaster(ai_2C_rast, crs = rob_proj)
AI_2C_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_2C_rob))
AI_2C_df_rob <- AI_2C_df_rob %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

AI_4C_rob <- projectRaster(ai_4C_rast, crs = rob_proj)
AI_4C_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_4C_rob))
AI_4C_df_rob <- AI_4C_df_rob %>%
    # Recode
dplyr::mutate(category = case_when(is.infinite(layer) ~ "Humid", layer >= 0.65 ~
    "Humid", layer >= 0.5 & layer < 0.65 ~ "Dry sub-humid", layer >= 0.2 & layer <
    0.5 ~ "Semi-arid", layer >= 0.05 & layer < 0.2 ~ "Arid", layer < 0.05 ~ "Hyper-arid")) %>%
    # Convert to ordered factor
dplyr::mutate(category = factor(category, levels = c("Hyper-arid", "Arid", "Semi-arid",
    "Dry sub-humid", "Humid"), ordered = TRUE))

arid_col <- c("#8E063B", "#CB6D53", "#E99A2C", "#F5D579", "white")
aridity_plot <- ggplot() + geom_raster(data = AI_df_rob, aes(y = y, x = x, fill = category)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = arid_col, guide = guide_legend(reverse = TRUE)) +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Aridity Index (1981-2010)") +
    scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0, 0)) + scale_x_continuous(limits = c(-1.5e+07,
    1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)

# Fig 1b - Species richness
anuran_rob <- projectRaster(anuran_sr, crs = rob_proj)
anuran_df_rob <- raster::as.data.frame(raster::rasterToPoints(anuran_rob)) %>%
    dplyr::rename(species_n = layer)

sp_breaks = c(1, 5, 25, 100)
anuran_plot <- ggplot() + geom_raster(data = anuran_df_rob, aes(y = y, x = x, fill = species_n)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + colorspace::scale_fill_continuous_sequential(palette = "BluGrn",
    trans = "log", breaks = sp_breaks, labels = sp_breaks) + ggnewscale::new_scale("fill") +
    geom_raster(data = hill_df %>%
        filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle("Anuran species richness") +
    scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0, 0)) + scale_x_continuous(limits = c(-1.5e+07,
    1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed()

AI_merged <- merge(AI_df_rob, AI_2C_df_rob, by = c("x", "y"), all.x = T) %>%
    merge(AI_4C_df_rob, by = c("x", "y"), all.x = T) %>%
    dplyr::rename(layer_current = layer.x, category_current = category.x, layer_2C = layer.y,
        category_2C = category.y, layer_4C = layer, category_4C = category) %>%
    dplyr::mutate(change_2C = (layer_2C - layer_current/layer_current) * 100, change_2C_AI = case_when(change_2C >=
        -2 & change_2C < 2 ~ "No change to wetter", change_2C >= -10 & change_2C <
        -2 ~ ">-2 to -10%", change_2C >= -20 & change_2C < -10 ~ ">-10 to -20%",
        change_2C >= -40 & change_2C < -20 ~ ">-20 to -40%", change_2C >= -80 & change_2C <
            -40 ~ ">-40 to -80%", change_2C < -80 ~ "<-80%"), change_4C = (layer_4C -
        layer_current/layer_current) * 100, change_4C_AI = case_when(change_4C >=
        -2 & change_4C < 2 ~ "No change to wetter", change_4C >= -10 & change_4C <
        -2 ~ ">-2 to -10%", change_4C >= -20 & change_4C < -10 ~ ">-10 to -20%",
        change_4C >= -40 & change_4C < -20 ~ ">-20 to -40%", change_4C >= -80 & change_4C <
            -40 ~ ">-40 to -80%", change_4C < -80 ~ "<-80%")) %>%
    filter(category_current != "Hyper-arid") %>%
    dplyr::mutate(change_2C_AI = factor(change_2C_AI, levels = c("<-80%", ">-40 to -80%",
        ">-20 to -40%", ">-10 to -20%", ">-2 to -10%", "No change to wetter"), ordered = TRUE),
        change_4C_AI = factor(change_4C_AI, levels = c("<-80%", ">-40 to -80%", ">-20 to -40%",
            ">-10 to -20%", ">-2 to -10%", "No change to wetter"), ordered = TRUE))

change_col <- c("#B13F63", "#D35D60", "#E38566", "#EEAC7D", "#F5D1A8", "white")

# Fig 1C - 2C Difference
change_2C_plot <- ggplot() + geom_raster(data = AI_merged, aes(y = y, x = x, fill = change_2C_AI)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = change_col, na.value = "white") +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in dryness (" *
    Delta * "AI +2°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

# Fig 1d - 4C Difference
change_4C_plot <- ggplot() + geom_raster(data = AI_merged, aes(y = y, x = x, fill = change_4C_AI)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = change_col, na.value = "white") +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in dryness (" *
    Delta * "AI +4°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)

# Fig 1e - Dryness and species richness
arid_col_2 <- c("#8E063B", "#CB6D53", "#E99A2C", "#F5D579", "light grey")
species_ai_plot <- ai_sp_df %>%
    ggplot(aes(x = aridity, y = species_n, colour = category)) + geom_point(size = 2) +
    coord_trans(x = "sqrt") + scale_colour_manual(values = arid_col_2, guide = guide_legend(reverse = TRUE)) +
    scale_x_continuous(limits = c(0, 3), breaks = c(0, 0.05, 0.2, 0.5, 0.65)) + xlab("Precipitation / Evapotranspiration") +
    ylab("Species richness") + mytheme() + theme(legend.position = "none", axis.text.x = element_blank(),
    axis.ticks.x = element_blank())

# Fig 1f - Dryness and species richness by ecotype
ecotype_ai_plot <- ai_sp_df %>%
    dplyr::filter(aridity <= 3) %>%
    dplyr::select(aridity, species_n:stream_n) %>%
    tidyr::pivot_longer(!aridity, names_to = "ecotype", values_to = "mean") %>%
    dplyr::filter(ecotype != "species_n") %>%
    drop_na(mean) %>%
    dplyr::mutate(ecotype = factor(ecotype, levels = c("fossorial_n", "ground_n",
        "aquatic_n", "arboreal_n", "semi_aq_n", "stream_n"))) %>%
    ggplot(aes(x = aridity, y = ecotype, group = ecotype, fill = ecotype)) + ggridges::stat_density_ridges(scale = 1,
    rel_min_height = 0.01, alpha = 0.5, show.legend = FALSE) + scale_fill_viridis_d() +
    scale_x_continuous(trans = "sqrt") + labs(x = NULL, y = NULL) + mytheme()

# Fig 1g - Change in species AI Merge summary data by row
arid_sp_comb <- data.frame(bind_rows(ai_sp_sum, ai_2C_sp_sum, ai_4C_sp_sum))
arid_sp_comb$scenario <- forcats::fct_relevel(arid_sp_comb$scenario, "Current")

species_occ_plot <- arid_sp_comb %>%
    ggplot(aes(x = category, y = log(all_freq), group = scenario, colour = scenario,
        shape = scenario)) + geom_point(position = position_dodge(width = 0.5), size = 1.5) +
    scale_colour_manual(values = c("#F5D579", "#CB6D53", "#8E063B")) + scale_y_continuous(breaks = pretty(log(arid_sp_comb$all_freq)),
    labels = pretty(arid_sp_comb$all_freq)) + ylab("% species") + xlab(NULL) + mytheme() +
    theme(legend.position = "none")

# Fig 1
plot_grid(species_ai_plot, ecotype_ai_plot, species_occ_plot, align = "v", ncol = 3)  # plot in between.
cowplot::plot_grid(aridity_plot + theme(legend.position = "bottom"), anuran_plot +
    theme(legend.position = "bottom"), change_2C_plot + theme(legend.position = "bottom"),
    change_4C_plot + theme(legend.position = "bottom"), align = "h", axis = "bt",
    ncol = 2)

Figure 2 - PDSI risk

PDSI_risk_rob <- raster::projectRaster(raster::stack(PDSI_2C_diff_rast, PDSI_4C_diff_rast, PDSI_freq_diff, PDSI_dur_diff,
                                                     resample(anuran_sr, PDSI_4C_diff_rast)), 
                                       crs = rob_proj)
                             
PDSI_risk_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_risk_rob)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(delta_int_2C_bin = ifelse(delta_int_2C < -1, 1, 0), # delta_PDSI < -1
         delta_int_4C_bin = ifelse(delta_int_4C < -1, 1, 0), # delta_PDSI < -1
         delta_freq_2C_bin = ifelse(delta_freq_2C >1, 1, 0), # month > 1
         delta_freq_4C_bin = ifelse(delta_freq_4C >1, 1, 0), # month > 1
         delta_dur_2C_bin = ifelse(delta_dur_2C >1, 1, 0), # month > 1
         delta_dur_4C_bin = ifelse(delta_dur_4C >1, 1, 0)) %>% # month > 1 
  dplyr::rowwise() %>%
  dplyr::mutate(count_2C = sum(delta_int_2C_bin, delta_freq_2C_bin, delta_dur_2C_bin, na.rm = T),
         
         count_4C = sum(delta_int_4C_bin, delta_freq_4C_bin, delta_dur_4C_bin, na.rm = T),
         risk_2C  = sp_n * count_2C,
         risk_4C  = sp_n * count_4C,
         count_2C = factor(count_2C, levels = c("3", "2", "1")),
         count_4C = factor(count_4C, levels = c("3", "2", "1"))
         ) %>%
  data.frame()

# Plot
PDSI_risk_2C_plot <- ggplot() +
  geom_raster(data = PDSI_risk_df, aes(y = y, x = x, fill = count_2C)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "RedOr", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("+2°C by 2080-2100")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

PDSI_risk_4C_plot <- ggplot() +
  geom_raster(data = PDSI_risk_df, aes(y = y, x = x, fill = count_4C)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "RedOr", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("+4°C by 2080-2100")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

sp_2C_plot <- PDSI_risk_df %>%
  dplyr::mutate(risk_2C_cat = case_when(
    risk_2C == 0 ~ '0',
    risk_2C > 0 & risk_2C <=1 ~ '<1',
    risk_2C > 1 & risk_2C < 5 ~ '>1 to 5',
    risk_2C >= 5 & risk_2C < 10 ~ '>5 to 10',
    risk_2C >= 10 & risk_2C < 20 ~ '>10 to 20',
    risk_2C >= 20 & risk_2C < 100 ~ '>20 to 100',
    risk_2C >= 100 & risk_2C < 200 ~ '>100 to 200',
    risk_2C > 200 ~ '>200'),
    risk_2C = na_if(risk_2C, 0),
    risk_2C_cat = factor(risk_2C_cat,
                                   levels = c('>200','>100 to 200', '>20 to 100','>10 to 20', '>5 to 10', '>1 to 5', '<1'))) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = risk_2C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "Heat", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

sp_4C_plot <- PDSI_risk_df %>%
  dplyr::mutate(risk_4C_cat = case_when(
    risk_4C == 0 ~ '0',
    risk_4C > 0 & risk_4C <=1 ~ '<1',
    risk_4C > 1 & risk_4C < 5 ~ '>1 to 5',
    risk_4C >= 5 & risk_4C < 10 ~ '>5 to 10',
    risk_4C >= 10 & risk_4C < 20 ~ '>10 to 20',
    risk_4C >= 20 & risk_4C < 100 ~ '>20 to 100',
    risk_4C >= 100 & risk_4C < 200 ~ '>100 to 200',
    risk_4C > 200 ~ '>200'),
    risk_4C = na_if(risk_4C, 0),
    risk_4C_cat = factor(risk_4C_cat,
                                   levels = c('>200','>100 to 200', '>20 to 100','>10 to 20', '>5 to 10', '>1 to 5', '<1'))) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = risk_4C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "Heat", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

cowplot::plot_grid(PDSI_risk_2C_plot + theme(legend.position = "none"),
                   PDSI_risk_4C_plot + theme(legend.position = "none"),
                   sp_2C_plot + theme(legend.position = "none"),
                   sp_4C_plot + theme(legend.position = "none"),
                   ncol = 2,
                   align = "h", axis = "bt", labels = c('a', 'b', 'c', 'd'))
#PDSI_risk_df %>%
  #group_by(count_2C) %>%
  #summarise(length = length(count_2C)) %>%
  #mutate(freq = length / length(PDSI_risk_df$count_2C) * 100)

#PDSI_risk_df %>%
  #group_by(count_4C) %>%
  #summarise(length = length(count_4C)) %>%
  #mutate(freq = length / length(PDSI_risk_df$count_4C) * 100)

Figure 3 - Water loss

# Fig. 3a - EWL
lnVPD_conditions <- list(lnVPD = setNames(c(-4.60517, -2.302585, 0, 2.302585), 
                                        c(round(exp(-4.60517), digits = 2), 
                                          round(exp(-2.302585), digits = 1), 
                                          exp(0), 
                                          round(exp(2.302585), digits = 1))))

ewl_ce <- conditional_effects(ewl_model, effects = "lnMass:lnVPD", int_conditions = lnVPD_conditions)

ewl_ce <- data.frame(ewl_ce[[1]]) %>%
  dplyr::rename(estimate = estimate__,
                VPD_cal  = effect2__) %>%
  dplyr::mutate(mean_mass_g = exp(lnMass),
         VPD_kPa     = exp(lnVPD),
         VPD_cal     = as.numeric(as.character(VPD_cal)))

ewl_mass_plot <- ewl_dat %>%
  ggplot() +
  geom_point(aes(x = mean_mass_g, y = mg_h_mean, shape = strategy), size = 2, colour = "grey") +
  geom_line(data = ewl_ce, aes(x = mean_mass_g, y = exp(estimate), group = lnVPD, colour = lnVPD), size = 1) +
  geom_text(data = ewl_ce %>% filter(mean_mass_g == last(mean_mass_g)), 
            aes(label = VPD_cal, x = mean_mass_g + 50, y = exp(estimate), hjust = -0.01),
            size = 3) +
  labs(x = "Body mass (g)", colour = "VPD (kPa)", shape = NULL) +
  ylab(expression("EWL"~("mg"~H[2]*O~h^{"-1"}))) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(0.1, 1, 10, 100, 1000), labels = c(0.1, 1, 10, 100, "1,000")) +
  scale_x_continuous(trans = 'log10') +
  expand_limits(x = 500) +
  scale_colour_gradient2(low = "#255668", mid = "#17A77E", high = "#82CC6C", 
                         midpoint = -1,
                         breaks = c(-4.60517, -2.302585, 0, 2.302585), 
                         labels = c(round(exp(-4.60517), digits = 2), 
                                     round(exp(-2.302585), digits = 1), 
                                     exp(0), 
                                     round(exp(2.302585), digits = 1))) +
  scale_shape_manual(values = c(16, 1, 2, 0)) +
  mytheme() + theme(legend.position = "right") +
  guides(colour = guide_colourbar(barheight = 5, barwidth = 0.5, label.position = "right"))

# Fig. 3b
# VPD
VPD_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_vpd.nc')), anuran_sr) # VPD year 1981-2010 (kPa)
VPD_mean_rast <- raster::calc(VPD_rast, fun = mean, na.rm = TRUE) 
names(VPD_mean_rast) <-"VPD"

# Wind speed
ws_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_ws.nc')), anuran_sr) # Wind speed year 1981-2010 (m/s)
ws_mean_rast <- raster::calc(ws_rast, fun = mean, na.rm = TRUE) 
names(ws_mean_rast) <-"wind_speed"

EWL_risk_rob <- raster::projectRaster(raster::stack(VPD_mean_rast, ws_mean_rast,
                                                    resample(anuran_sr, VPD_mean_rast)), 
                                       crs = rob_proj)

# Estimate EWL for a 30 g ground-dwelling frog
VPD_sp_df <- raster::as.data.frame(raster::rasterToPoints(EWL_risk_rob)) %>% 
  rename(species_n = layer) %>%
  drop_na(species_n) %>%
  dplyr::mutate(wind_speed_cm_s = wind_speed * 100, # from m/s to cm/s
                u = 0.4 * wind_speed_cm_s / log((100 / 0.15) + 1), # u = friction velocity
                wind_speed_corr = 2.5 * u * log((1 / 0.15) + 1), # correct for reference height (10 m) to ground level (1 cm) assuming level ground
                EWL_pred = 3.74 - 0.11 + (log(Ww_g) * 0.55) + (log(VPD) * 0.28) + (log(wind_speed_corr) * 0.34),
                EWL_pred = exp(EWL_pred) * 0.001 # from mg/h to g/h
                )

VPD_sp_plot <- VPD_sp_df %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = EWL_pred)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "YlGnBu", rev = T, 
                                   name = expression("EWL"~("g"~H[2]*O~h^{"-1"}))) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  guides(fill = guide_colourbar(barwidth = 20, barheight = 0.2, 
                                label.position = "bottom")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.position = "bottom") +
  coord_fixed(ratio = 1) # fixed ratio

cowplot::plot_grid(ewl_mass_plot,
                   VPD_sp_plot,
                   ncol = 1,
                   align = "h", axis = "bt", labels = c('a', 'b'), rel_heights = c(0.8, 1))

Figure 4 - Activity risk

shad_plot <- shad_model %>%
    ggplot(aes(x = day, y = condition, fill = hours)) + geom_tile() + scale_fill_continuous_diverging(palette = "Blue-Red 3",
    mid = 12, rev = TRUE) + ylab(NULL) + xlab("Day of the year") + scale_x_continuous(expand = c(0,
    0)) + mytheme() + theme(legend.position = "bottom") + guides(fill = guide_colourbar(barheight = 0.5,
    barwidth = 5, label.position = "bottom"))

tree_plot <- tree_model %>%
    ggplot(aes(x = day, y = condition, fill = hours)) + geom_tile() + scale_fill_continuous_diverging(palette = "Blue-Red 3",
    mid = 12, rev = TRUE) + ylab(NULL) + xlab("Day of the year") + scale_x_continuous(expand = c(0,
    0)) + mytheme() + theme(legend.position = "bottom") + guides(fill = guide_colourbar(barheight = 0.5,
    barwidth = 5, label.position = "bottom"))

burr_plot <- burr_model %>%
    ggplot(aes(x = day, y = condition, fill = hours)) + geom_tile() + scale_fill_continuous_diverging(palette = "Blue-Red 3",
    mid = 12, rev = TRUE) + ylab(NULL) + xlab("Day of the year") + scale_x_continuous(expand = c(0,
    0)) + mytheme() + theme(legend.position = "bottom") + guides(fill = guide_colourbar(barheight = 0.5,
    barwidth = 5, label.position = "bottom"))

prow_2 <- cowplot::plot_grid(shad_plot + theme(legend.position = "none"), tree_plot +
    theme(legend.position = "none"), burr_plot + theme(legend.position = "none"),
    ncol = 1, labels = c("a", "b", "c"), align = "v", axis = "l")

legend_b_2 <- cowplot::get_legend(burr_plot + guides(color = guide_legend(nrow = 1)))

plot_grid(prow_2, legend_b_2, nrow = 1, rel_heights = c(1, 0.1))

Extended data

Extended Figure 1 - PDSI intensity risk

PDSI_2C_diff_rast_crop <- raster::mask(crop(PDSI_2C_diff_rast, extent(world)), world)  # crop 
PDSI_2C_rob <- raster::projectRaster(raster::stack(PDSI_2C_diff_rast_crop, resample(anuran_sr,
    PDSI_2C_diff_rast_crop)), crs = rob_proj)

PDSI_4C_diff_rast_crop <- raster::mask(crop(PDSI_4C_diff_rast, extent(world)), world)  # crop 
PDSI_4C_rob <- raster::projectRaster(raster::stack(PDSI_4C_diff_rast_crop, resample(anuran_sr,
    PDSI_4C_diff_rast_crop)), crs = rob_proj)

PDSI_2C_df_rob <- raster::as.data.frame(raster::rasterToPoints(PDSI_2C_rob)) %>%
    dplyr::rename(layer = delta_int_2C, species_n = layer) %>%
    dplyr::mutate(change = case_when(layer >= 4 ~ "4", layer >= 3 & layer < 4 ~ "3",
        layer >= 2 & layer < 3 ~ "2", layer >= 1 & layer < 2 ~ "1", layer >= -1 &
            layer < 1 ~ "0", layer >= -2 & layer < -1 ~ "-1", layer >= -3 & layer <
            -2 ~ "-2", layer >= -4 & layer < -3 ~ "-3", layer < -4 ~ "-4")) %>%
    dplyr::mutate(change = factor(change, levels = c("-4", "-3", "-2", "-1", "0",
        "1", "2", "3", "4"), ordered = TRUE)) %>%
    dplyr::filter(change != "NA" & species_n != "NA")

PDSI_4C_df_rob <- raster::as.data.frame(raster::rasterToPoints(PDSI_4C_rob)) %>%
    dplyr::rename(layer = delta_int_4C, species_n = layer) %>%
    dplyr::mutate(change = case_when(layer >= 4 ~ "4", layer >= 3 & layer < 4 ~ "3",
        layer >= 2 & layer < 3 ~ "2", layer >= 1 & layer < 2 ~ "1", layer >= -1 &
            layer < 1 ~ "0", layer >= -2 & layer < -1 ~ "-1", layer >= -3 & layer <
            -2 ~ "-2", layer >= -4 & layer < -3 ~ "-3", layer < -4 ~ "-4")) %>%
    dplyr::mutate(change = factor(change, levels = c("-4", "-3", "-2", "-1", "0",
        "1", "2", "3", "4"), ordered = TRUE)) %>%
    dplyr::filter(change != "NA" & species_n != "NA")

# Intensity +2C - Ex Fig. 1a
colours_PDSI <- RColorBrewer::brewer.pal(9, "RdBu")
PDSI_2C_plot <- ggplot() + geom_raster(data = PDSI_2C_df_rob, aes(y = y, x = x, fill = change)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = colours_PDSI) + ggnewscale::new_scale("fill") +
    geom_raster(data = hill_df %>%
        filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression(Delta * "PDSI intensity (+2°C)")) +
    scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0, 0)) + scale_x_continuous(limits = c(-1.5e+07,
    1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

# Intensity +4C - Ex Fig. 1c
PDSI_4C_plot <- ggplot() + geom_raster(data = PDSI_4C_df_rob, aes(y = y, x = x, fill = change)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = colours_PDSI) + ggnewscale::new_scale("fill") +
    geom_raster(data = hill_df %>%
        filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression(Delta * "PDSI intensity (+4°C)")) +
    scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0, 0)) + scale_x_continuous(limits = c(-1.5e+07,
    1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(), axis.text = element_blank(),
    axis.ticks = element_blank(), plot.title = element_text(size = 10)) + coord_fixed(ratio = 1)  # fixed ratio

# 2C change - Ex Fig 1b
PDSI_sp_2C_plot <- PDSI_sp_2C %>%
    rows_insert(tibble(change_2C = "4", all_freq = 0), conflict = "ignore") %>%
    filter(change_2C != "NA") %>%
    ggplot(aes(x = change_2C, y = all_freq, fill = change_2C)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = colours_PDSI) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("PDSI") + ylim(0, 80) +
    ggtitle("Grid cell occupied +2°C") + mytheme() + theme(plot.title = element_text(size = 10))

# 4C change - Ex Fig 1d
PDSI_sp_4C_plot <- PDSI_sp_4C %>%
    filter(change_4C != "NA") %>%
    ggplot(aes(x = change_4C, y = all_freq, fill = change_4C)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = colours_PDSI) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("PDSI") + ylim(0, 80) +
    ggtitle("Grid cell occupied +4°C") + mytheme() + theme(plot.title = element_text(size = 10))

left_plot <- cowplot::plot_grid(PDSI_2C_plot + theme(legend.position = "none"), PDSI_4C_plot +
    theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt", labels = c("a",
    "c", "e"))

right_plot <- cowplot::plot_grid(PDSI_sp_2C_plot + theme(legend.position = "none"),
    PDSI_sp_4C_plot + theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt",
    labels = c("b", "d", "f"))

cowplot::plot_grid(left_plot, right_plot, ncol = 2, rel_widths = c(1, 0.7))

Extended Figure 2 - PDSI frequency risk

freq_diff_rast_crop <- raster::mask(crop(freq_diff_rast, extent(world)), world)  # crop

PDSI_freq_rob <- raster::projectRaster(raster::stack(freq_diff_rast_crop, resample(anuran_sr,
    freq_diff_rast_crop)), crs = rob_proj)

PDSI_freq_2C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_freq_rob)) %>%
    dplyr::mutate(diff_2C_cat = case_when(diff_2C >= 10 ~ "10-12", diff_2C >= 8 &
        diff_2C < 10 ~ "8-10", diff_2C >= 6 & diff_2C < 8 ~ "6-8", diff_2C >= 4 &
        diff_2C < 6 ~ "4-6", diff_2C >= 2 & diff_2C < 4 ~ "2-4", diff_2C >= 1 & diff_2C <
        2 ~ "1-2", diff_2C > 0 & diff_2C < 1 ~ "0-1", diff_2C >= -1 & diff_2C < 0 ~
        "-0-1", diff_2C >= -2 & diff_2C < -1 ~ "-1-2", diff_2C >= -4 & diff_2C <
        -2 ~ "-2-4")) %>%
    dplyr::mutate(diff_2C_cat = factor(diff_2C_cat, levels = c("10-12", "8-10", "6-8",
        "4-6", "2-4", "1-2", "0-1", "-0-1", "-1-2", "-2-4"), ordered = TRUE)) %>%
    dplyr::filter(diff_2C_cat != "NA" & layer != "NA")

PDSI_freq_4C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_freq_rob)) %>%
    dplyr::mutate(diff_4C_cat = case_when(diff_4C >= 10 ~ "10-12", diff_4C >= 8 &
        diff_4C < 10 ~ "8-10", diff_4C >= 6 & diff_4C < 8 ~ "6-8", diff_4C >= 4 &
        diff_4C < 6 ~ "4-6", diff_4C >= 2 & diff_4C < 4 ~ "2-4", diff_4C >= 1 & diff_4C <
        2 ~ "1-2", diff_4C > 0 & diff_4C < 1 ~ "0-1", diff_4C >= -1 & diff_4C < 0 ~
        "-0-1", diff_4C >= -2 & diff_4C < -1 ~ "-1-2", diff_4C >= -4 & diff_4C <
        -2 ~ "-2-4")) %>%
    dplyr::mutate(diff_4C_cat = factor(diff_4C_cat, levels = c("10-12", "8-10", "6-8",
        "4-6", "2-4", "1-2", "0-1", "-0-1", "-1-2", "-2-4"), ordered = TRUE)) %>%
    dplyr::filter(diff_4C_cat != "NA" & layer != "NA")

# Freq map 2C - Fig S10a
freq_2C_plot <- ggplot() + geom_raster(data = PDSI_freq_2C_df, aes(y = y, x = x,
    fill = diff_2C_cat)) + geom_polygon(data = world_rob, aes(x = long, y = lat,
    group = group), colour = "#64686b", fill = NA, size = 0.1) + scale_fill_manual(values = c("#67001F",
    "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in drought frequency (" *
    Delta * "PDSI +2°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)  # fixed ratio

# Freq map 4C - Fig S10c
freq_4C_plot <- ggplot() + geom_raster(data = PDSI_freq_4C_df, aes(y = y, x = x,
    fill = diff_4C_cat)) + geom_polygon(data = world_rob, aes(x = long, y = lat,
    group = group), colour = "#64686b", fill = NA, size = 0.1) + scale_fill_manual(values = c("#67001F",
    "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in drought frequency (" *
    Delta * "PDSI +4°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)  # fixed ratio

# 2C change - Fig S10b
freq_sp_2C <- data.frame(PDSI_freq_2C_df %>%
    dplyr::group_by(diff_2C_cat) %>%
    dplyr::summarise(species_n = length(layer[!is.na(layer)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100)

freq_sp_2C_plot <- freq_sp_2C %>%
    ggplot(aes(x = diff_2C_cat, y = all_freq, fill = diff_2C_cat)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7", "#dfebf2")) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("Months") + ylim(0, 60) +
    ggtitle("Grid cell occupied +2°C") + mytheme() + theme(plot.title = element_text(size = 10))

# 4C change - Fig S10d
freq_sp_4C <- data.frame(PDSI_freq_4C_df %>%
    dplyr::group_by(diff_4C_cat) %>%
    dplyr::summarise(species_n = length(layer[!is.na(layer)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100)

freq_sp_4C_plot <- freq_sp_4C %>%
    ggplot(aes(x = diff_4C_cat, y = all_freq, fill = diff_4C_cat)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7", "#dfebf2")) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("Months") + ylim(0, 60) +
    ggtitle("Grid cell occupied +4°C") + mytheme() + theme(plot.title = element_text(size = 10))

left_plot2 <- cowplot::plot_grid(freq_2C_plot + theme(legend.position = "none"),
    freq_4C_plot + theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt",
    labels = c("a", "c"))

right_plot2 <- cowplot::plot_grid(freq_sp_2C_plot + theme(legend.position = "none"),
    freq_sp_4C_plot + theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt",
    labels = c("b", "d"))

cowplot::plot_grid(left_plot2, right_plot2, ncol = 2, rel_widths = c(1, 0.7))

Extended Figure 3 - PDSI duration risk

PDSI_dur_diff_crop <- raster::mask(crop(PDSI_dur_diff, extent(world)), world)  # crop

PDSI_dur_rob <- raster::projectRaster(raster::stack(PDSI_dur_diff_crop, resample(anuran_sr,
    PDSI_dur_diff_crop)), crs = rob_proj)

PDSI_dur_2C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_dur_rob)) %>%
    dplyr::rename(sp_n = layer) %>%
    dplyr::filter(!is.na(sp_n)) %>%
    dplyr::mutate(diff_2C_cat = case_when(delta_dur_2C >= 10 ~ ">10", delta_dur_2C >=
        8 & delta_dur_2C < 10 ~ "8-10", delta_dur_2C >= 6 & delta_dur_2C < 8 ~ "6-8",
        delta_dur_2C >= 4 & delta_dur_2C < 6 ~ "4-6", delta_dur_2C >= 2 & delta_dur_2C <
            4 ~ "2-4", delta_dur_2C >= 1 & delta_dur_2C < 2 ~ "1-2", delta_dur_2C >
            0 & delta_dur_2C < 1 ~ "0-1", delta_dur_2C >= -1 & delta_dur_2C < 0 ~
            "-0-1", delta_dur_2C >= -2 & delta_dur_2C < -1 ~ "-1-2", delta_dur_2C >=
            -4 & delta_dur_2C < -2 ~ "-2-4", delta_dur_2C >= -8 & delta_dur_2C <
            -6 ~ "-6-8", delta_dur_2C >= -10 & delta_dur_2C < -8 ~ "-8-10", delta_dur_2C <
            -10 ~ "<-10")) %>%
    dplyr::mutate(diff_2C_cat = factor(diff_2C_cat, levels = c(">10", "8-10", "6-8",
        "4-6", "2-4", "1-2", "0-1", "-0-1", "-1-2", "-2-4", "-4-6", "-6-8", "-8-10",
        "<-10"), ordered = TRUE)) %>%
    dplyr::filter(diff_2C_cat != "NA")

PDSI_dur_4C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_dur_rob)) %>%
    dplyr::rename(sp_n = layer) %>%
    dplyr::filter(!is.na(sp_n)) %>%
    dplyr::mutate(diff_4C_cat = case_when(delta_dur_4C >= 10 ~ ">10", delta_dur_4C >=
        8 & delta_dur_4C < 10 ~ "8-10", delta_dur_4C >= 6 & delta_dur_4C < 8 ~ "6-8",
        delta_dur_4C >= 4 & delta_dur_4C < 6 ~ "4-6", delta_dur_4C >= 2 & delta_dur_4C <
            4 ~ "2-4", delta_dur_4C >= 1 & delta_dur_4C < 2 ~ "1-2", delta_dur_4C >
            0 & delta_dur_4C < 1 ~ "0-1", delta_dur_4C >= -1 & delta_dur_4C < 0 ~
            "-0-1", delta_dur_4C >= -2 & delta_dur_4C < -1 ~ "-1-2", delta_dur_4C >=
            -4 & delta_dur_4C < -2 ~ "-2-4", delta_dur_4C >= -6 & delta_dur_4C <
            -4 ~ "-4-6", delta_dur_4C >= -8 & delta_dur_4C < -6 ~ "-6-8", delta_dur_4C >=
            -10 & delta_dur_4C < -8 ~ "-8-10", delta_dur_4C < -10 ~ "<-10")) %>%
    dplyr::mutate(diff_4C_cat = factor(diff_4C_cat, levels = c(">10", "8-10", "6-8",
        "4-6", "2-4", "1-2", "0-1", "-0-1", "-1-2", "-2-4", "-4-6", "-6-8", "-8-10",
        "<-10"), ordered = TRUE)) %>%
    dplyr::filter(diff_4C_cat != "NA")

dur_2C_plot <- ggplot() + geom_raster(data = PDSI_dur_2C_df, aes(y = y, x = x, fill = diff_2C_cat)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = c("#67001F", "#B2182B",
    "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in drought duration (" *
    Delta * "PDSI +2°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)  # fixed ratio

dur_4C_plot <- ggplot() + geom_raster(data = PDSI_dur_4C_df, aes(y = y, x = x, fill = diff_4C_cat)) +
    geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b",
        fill = NA, size = 0.1) + scale_fill_manual(values = c("#67001F", "#B2182B",
    "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
    ggnewscale::new_scale("fill") + geom_raster(data = hill_df %>%
    filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
    scale_fill_gradient(low = "black", high = "grey") + scale_alpha_continuous(trans = "reverse") +
    theme_void() + ylab(NULL) + xlab(NULL) + ggtitle(expression("Change in drought duration (" *
    Delta * "PDSI +4°C)")) + scale_y_continuous(limits = c(-6100000, 8500000), expand = c(0,
    0)) + scale_x_continuous(limits = c(-1.5e+07, 1.6e+07), expand = c(0, 0)) + theme(axis.title = element_blank(),
    axis.text = element_blank(), axis.ticks = element_blank(), plot.title = element_text(size = 10)) +
    coord_fixed(ratio = 1)  # fixed ratio

# 2C change - Fig S11b
dur_sp_2C <- data.frame(PDSI_dur_2C_df %>%
    dplyr::group_by(diff_2C_cat) %>%
    dplyr::summarise(species_n = length(sp_n[!is.na(sp_n)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100)

dur_sp_2C_plot <- dur_sp_2C %>%
    ggplot(aes(x = diff_2C_cat, y = all_freq, fill = diff_2C_cat)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("Months") + ylim(0, 50) +
    ggtitle("Grid cell occupied +2°C") + mytheme() + theme(plot.title = element_text(size = 10))

# 4C change - Fig S11d
dur_sp_4C <- data.frame(PDSI_dur_4C_df %>%
    dplyr::group_by(diff_4C_cat) %>%
    dplyr::summarise(species_n = length(sp_n[!is.na(sp_n)]))) %>%
    dplyr::mutate(all_freq = species_n/sum(species_n) * 100)

dur_sp_4C_plot <- dur_sp_4C %>%
    ggplot(aes(x = diff_4C_cat, y = all_freq, fill = diff_4C_cat)) + geom_bar(stat = "identity") +
    scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7",
        "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) + geom_text(aes(label = round(all_freq,
    1)), vjust = -1, size = 2) + ylab("% species") + xlab("Months") + ylim(0, 50) +
    ggtitle("Grid cell occupied +4°C") + mytheme() + theme(plot.title = element_text(size = 10))

left_plot3 <- cowplot::plot_grid(dur_2C_plot + theme(legend.position = "none"), dur_4C_plot +
    theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt", labels = c("a",
    "c"))

right_plot3 <- cowplot::plot_grid(dur_sp_2C_plot + theme(legend.position = "none"),
    dur_sp_4C_plot + theme(legend.position = "none"), ncol = 1, align = "h", axis = "bt",
    labels = c("b", "d"))

cowplot::plot_grid(left_plot3, right_plot3, ncol = 2, rel_widths = c(1, 0.7))

Extended Figure 4 - Water uptake

hydr_conditions <- list(hydration = setNames(c(70, 80, 90, 100), c(70, 80, 90, 100)))
wu_ce <- conditional_effects(wu_model, effects = "lnMass:hydration", int_conditions = hydr_conditions)

wu_ce <- data.frame(wu_ce[[1]]) %>%
    dplyr::rename(estimate = estimate__, hydration_cal = effect2__) %>%
    dplyr::mutate(mean_mass_g = exp(lnMass), hydration_cal = as.numeric(as.character(hydration_cal)))

wu_dat %>%
    ggplot() + geom_point(aes(x = mean_mass_g, y = mg_h_mean), size = 2, colour = "grey") +
    geom_line(data = wu_ce, aes(x = mean_mass_g, y = exp(estimate), group = hydration,
        colour = hydration), size = 1) + geom_text(data = wu_ce %>%
    filter(mean_mass_g == last(mean_mass_g)), aes(label = hydration, x = mean_mass_g +
    50, y = exp(estimate), hjust = -0.01), size = 3) + labs(x = "Body mass (g)",
    colour = "Initial hydration (%)") + ylab(expression("WU" ~ ("mg" ~ H[2] * O ~
    h^{
        "-1"
    }))) + scale_y_continuous(trans = scales::log_trans(), breaks = c(10, 100, 1000,
    10000, 1e+05), labels = c(10, 100, "1,000", "10,000", "100,000")) + scale_x_continuous(trans = "log10") +
    scale_colour_gradient2(low = "#0E3F5C", mid = "#2A6D7A", high = "#8FCCB4", midpoint = 85) +
    expand_limits(x = 500) + mytheme() + theme(legend.title = element_text(size = 8),
    legend.position = "bottom") + guides(colour = guide_colourbar(barheight = 0.5,
    barwidth = 10, title.position = "top"))

References

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Baldwin, R. A. (1974). The water balance response of the pelvic “patch” of Bufo punctatus and Bufo boreas. Comparative Biochemistry and Physiology Part A: Physiology 47, 1285–1295.
Blaylock, L. A., Ruibal, R. and Kathryn, P.-A. (1976). Skin structure and wiping behavior of phyllomedusine frogs. Copeia 1976, 283–295.
Bovo, R. P., Simon, M. N., Lyra, M. L., Navas, C. A. and Andrade, D. V. Beyond janzen’s hypothesis: How amphibians that climb tropical mountains respond to climate variation.
Budyko, M. I. (1961). The heat balance of the earth’s surface. Soviet Geography 2, 3–13.
Buttemer, W. A. and Thomas, C. (2003). Influence of temperature on evaporative water loss and cutaneous resistance to water vapour diffusion in the orange-thighed frog (Litoria xanthomera). Australian Journal of Zoology 51, 111–118.
Feder, M. E. and Burggren, W. W. (1992). Environmental physiology of the amphibians. Chicago, USA: University of Chicago Press.
Gomez, N. A., Acosta, M., Zaidan III, F. and Lillywhite, H. B. (2006). Wiping behavior, skin resistance, and the metabolic response to dehydration in the arboreal frog Phyllomedusa hypochondrialis. Current Biology 79, 1058–1068.
Gouveia, S. F., Rafael, R. P., Rubalcaba, J. G., Da Silva, F. R., Maciel, N. M., Andrade, D. V. and Martinez, P. A. (2019). Biophysical modeling of water economy can explain geographic gradient of body size in anurans. The American Naturalist 193, 51–58.
Hillman, S. S., Withers, P. C., Drewes, R. C. and Hillyard, S. D. (2009). Ecological and environmental physiology of amphibians. New York, USA: Oxford University Press.
Jetz, W. and Pyron, R. A. (2018). The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nature Ecology & Evolution 2, 850–858.
Kearney, M. R. and Porter, W. P. (2017). NicheMapR–an R package for biophysical modelling: The microclimate model. Ecography 40, 664–674.
Kearney, M. R. and Porter, W. P. (2020). NicheMapR–an R package for biophysical modelling: The ectotherm and dynamic energy budget models. Ecography 43, 85–96.
Kearney, M. R., Phillips, B. L., Tracy, C. R., Christian, K. A., Betts, G. and Porter, W. P. (2008). Modelling species distributions without using species distributions: The cane toad in australia under current and future climates. Ecography 31, 423–434.
Kearney, M. R., Shamakhy, A., Tingley, R., Karoly, D. J., Hoffmann, A. A., Briggs, P. R. and Porter, W. P. (2014). Microclimate modelling at macro scales: A test of a general microclimate model integrated with gridded continental‐scale soil and weather data. Methods in Ecology and Evolution 5, 273–286.
Klein, W., Dabés, L., Bonfim, V. M. G., Magrini, L. and Napoli, M. F. (2016). Allometric relationships between cutaneous surface area and body mass in anuran amphibians. Zoologischer Anzeiger-A Journal of Comparative Zoology 263, 45–54.
Kobelt, F. and Linsenmair, K. E. (1986). Adaptations of the reed frog Hyperolius viridiflavus (amphibia, anura, hyperoliidae) to its arid environment i. The skin of Hyperolius viridiflavus nitidulus in wet and dry season conditions. Oecologia 68, 533–541.
Lillywhite, H. B. (2006). Water relations of tetrapod integument. Journal of Experimental Biology 209, 202–226.
McClanahan Jr, L. and Baldwin, R. (1969). Rate of water uptake through the integument of the desert toad, Bufo punctatus. Comparative Biochemistry and Physiology 28, 381–389.
Moen, D. S. and Wiens, J. J. (2017). Microhabitat and climatic niche change explain patterns of diversification among frog families. The American Naturalist 190, 29–44.
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Ouzzani, M., Hammady, H., Fedorowicz, Z. and Elmagarmid, A. (2016). Rayyan—a web and mobile app for systematic reviews. Systematic reviews 5, 1–10.
Palmer, W. C. (1965). Meteorological drought. Washington DC, USA: US Department of Commerce, Weather Bureau.
Pirtle, E. I., Tracy, C. R. and Kearney, R., Michael (2019). Hydroregulation: A neglected behavioral response of lizards to climate change? In Behavior of lizards, pp. 343–374. New York, USA: CRC Press.
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Senzano, L. M., Bovo, R. P. and Andrade, D. V. (2022). Empirical estimation of skin resistance to water loss in amphibians: Agar evaluation as a non-resistance model to evaporation. Journal of Experimental Biology 225, jeb243941.
Spotila, J. R. and Berman, E. N. (1976). Determination of skin resistance and the role of the skin in controlling water loss in amphibians and reptiles. Comparative Biochemistry and Physiology Part A: Physiology 55, 407–411.
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Titon Jr, B. and Gomes, F. R. (2015). Relation between water balance and climatic variables associated with the geographical distribution of anurans. PLOS One 10, e0140761.
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Withers, P. C., Hillman, S. S., Drewes, R. and Sokol, O. (1982). Water loss and nitrogen excretion in sharp-nosed reed frogs (Hyperolius nasutus: Anura, Hyperoliidae). Journal of Experimental Biology 97, 335–343.
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Session Information

R version 4.2.2 (2022-10-31)

Platform: aarch64-apple-darwin20 (64-bit)

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: rasterSp(v.0.0.1), tmaptools(v.3.1-1), tmap(v.3.3-4), sf(v.1.0-14), raster(v.3.6-26), sp(v.2.1-1), ncdf4(v.1.21), NicheMapR(v.3.2.1), ggtree(v.3.4.1), phytools(v.1.9-16), maps(v.3.4.1.1), ape(v.5.7-1), performance(v.0.10.8), cmdstanr(v.0.5.3), rstan(v.2.32.3), StanHeaders(v.2.26.28), brms(v.2.20.4), Rcpp(v.1.0.11), ggnewscale(v.0.4.9), cowplot(v.1.1.1), RColorBrewer(v.1.1-3), colorspace(v.2.1-0), lubridate(v.1.9.3), forcats(v.1.0.0), stringr(v.1.5.0), dplyr(v.1.1.3), purrr(v.1.0.2), readr(v.2.1.4), tidyr(v.1.3.0), tibble(v.3.2.1), ggplot2(v.3.4.4), tidyverse(v.2.0.0), Matrix(v.1.5-4.1), pander(v.0.6.5) and knitr(v.1.45)

loaded via a namespace (and not attached): pacman(v.0.5.1), utf8(v.1.2.4), tidyselect(v.1.2.0), htmlwidgets(v.1.6.2), grid(v.4.2.2), combinat(v.0.0-8), munsell(v.0.5.0), codetools(v.0.2-19), units(v.0.8-4), DT(v.0.30), miniUI(v.0.1.1.1), withr(v.2.5.2), Brobdingnag(v.1.2-9), highr(v.0.10), rstudioapi(v.0.15.0), stats4(v.4.2.2), bayesplot(v.1.10.0), labeling(v.0.4.3), emmeans(v.1.8.9), polyclip(v.1.10-6), mnormt(v.2.1.1), optimParallel(v.1.0-2), farver(v.2.1.1), bridgesampling(v.1.1-2), coda(v.0.19-4), vctrs(v.0.6.4), treeio(v.1.20.1), generics(v.0.1.3), clusterGeneration(v.1.3.8), xfun(v.0.41), timechange(v.0.2.0), R6(v.2.5.1), markdown(v.1.11), doParallel(v.1.0.17), cachem(v.1.0.8), gridGraphics(v.0.5-1), promises(v.1.2.1), scales(v.1.2.1), gtable(v.0.3.4), lwgeom(v.0.2-13), processx(v.3.8.2), phangorn(v.2.11.1), rlang(v.1.1.2), scatterplot3d(v.0.3-44), splines(v.4.2.2), rgdal(v.1.6-7), lazyeval(v.0.2.2), dichromat(v.2.0-0.1), checkmate(v.2.3.0), inline(v.0.3.19), yaml(v.2.3.7), reshape2(v.1.4.4), abind(v.1.4-5), threejs(v.0.3.3), crosstalk(v.1.2.0), backports(v.1.4.1), httpuv(v.1.6.12), tensorA(v.0.36.2), tools(v.4.2.2), bookdown(v.0.36), ggplotify(v.0.1.2), ellipsis(v.0.3.2), jquerylib(v.0.1.4), posterior(v.1.5.0), proxy(v.0.4-27), ggridges(v.0.5.4), plyr(v.1.8.9), base64enc(v.0.1-3), classInt(v.0.4-10), ps(v.1.7.5), prettyunits(v.1.2.0), zoo(v.1.8-12), leafem(v.0.2.3), fs(v.1.6.3), magrittr(v.2.0.3), data.table(v.1.14.8), colourpicker(v.1.3.0), mvtnorm(v.1.2-3), matrixStats(v.1.0.0), hms(v.1.1.3), patchwork(v.1.1.3), shinyjs(v.2.1.0), mime(v.0.12), evaluate(v.0.23), xtable(v.1.8-4), XML(v.3.99-0.15), shinystan(v.2.6.0), leaflet(v.2.2.0), gridExtra(v.2.3), rstantools(v.2.3.1.1), compiler(v.4.2.2), KernSmooth(v.2.23-22), V8(v.4.4.0), crayon(v.1.5.2), htmltools(v.0.5.7), mgcv(v.1.9-0), ggfun(v.0.1.3), later(v.1.3.1), tzdb(v.0.4.0), aplot(v.0.2.2), expm(v.0.999-7), RcppParallel(v.5.1.7), DBI(v.1.1.3), tweenr(v.2.0.2), formatR(v.1.14), MASS(v.7.3-60), cli(v.3.6.1), quadprog(v.1.5-8), parallel(v.4.2.2), insight(v.0.19.6), igraph(v.1.5.1), pkgconfig(v.2.0.3), numDeriv(v.2016.8-1.1), terra(v.1.7-55), foreach(v.1.5.2), dygraphs(v.1.1.1.6), QuickJSR(v.1.0.7), bslib(v.0.5.1), estimability(v.1.4.1), yulab.utils(v.0.1.0), distributional(v.0.3.2), callr(v.3.7.3), digest(v.0.6.33), rmarkdown(v.2.25), fastmatch(v.1.1-4), leafsync(v.0.1.0), tidytree(v.0.4.5), curl(v.5.1.0), shiny(v.1.7.5.1), gtools(v.3.9.4), lifecycle(v.1.0.3), nlme(v.3.1-163), jsonlite(v.1.8.7), viridisLite(v.0.4.2), fansi(v.1.0.5), pillar(v.1.9.0), lattice(v.0.22-5), loo(v.2.6.0), fastmap(v.1.1.1), plotrix(v.3.8-3), pkgbuild(v.1.4.2), glue(v.1.6.2), xts(v.0.13.1), bayestestR(v.0.13.1), png(v.0.1-8), shinythemes(v.1.2.0), iterators(v.1.0.14), ggforce(v.0.4.1), class(v.7.3-22), stringi(v.1.7.12), sass(v.0.4.7), stars(v.0.6-4), memoise(v.2.0.1) and e1071(v.1.7-13)

  1. Hawkesbury Institute for the Environment, Western Sydney University, NSW 2753, Australia, ↩︎

---
title: "Widespread exposure risk of anurans to rising global dryness"
subtitle: "Supplementary Information"
date: "`r format(Sys.Date(), '%d/%m/%Y')`"
author:
  - Nicholas C. Wu^[Hawkesbury Institute for the Environment, Western Sydney University, NSW 2753, Australia, nicholas.wu.nz@gmail.com]
output:
  bookdown::html_document2:
    code_download: yes
    code_folding: hide
    toc: yes
    toc_float: yes
    highlight: tango
editor_options:
  chunk_output_type: console
csl: ./bib/the-journal-of-experimental-biology.csl
bibliography: ./bib/drought_ref.bib
link-citations: true
---

[GitHub Repository](https://github.com/nicholaswunz/global-frog-drought)
The Rmarkdown file can be downloaded from the *Code* drop down menu (top right).

This supplementary file contains the *R* workflow for processing and analysing the raw data, and creating figures for the manuscript titled "**Global vulnerability of anurans to drought and warming**".

```{r setup, include=FALSE}
library(knitr)
knitr::opts_chunk$set(echo = TRUE, cache = FALSE, tidy = TRUE, message = FALSE, warning = FALSE)
options(dplyr.width = Inf, knitr.kable.NA = "", digits = 2)

# Load library
pacman::p_load(pander, Matrix, tidyverse, colorspace, RColorBrewer, cowplot, ggnewscale, 
               brms, rstan, cmdstanr, performance, ape, phytools, ggtree, NicheMapR,
               ncdf4, raster, sf, tmap, tmaptools, rasterSp)

# Functions
mytheme <- function() {
  theme_bw() +
    theme(panel.border          = element_rect(fill = NA, colour = "black"),
          panel.grid.major      = element_blank(),
          panel.grid.minor      = element_blank(),
          axis.line             = element_blank(),
          axis.ticks            = element_line(colour = "black"),
          axis.text             = element_text(size = 10, colour = "black"),
          axis.title            = element_text(size = 10),
          axis.title.y          = element_text(vjust = 3),
          axis.title.x          = element_text(vjust = -1),
          panel.background      = element_rect(fill = NA),
          plot.background       = element_rect(fill = NA, color = NA),
          plot.margin           = unit(c(0.2, 0.2, 0.2, 0.2), units = , "cm"),
          legend.background     = element_rect(fill = NA, color = NA),
          legend.box.background = element_rect(fill = NA, color = NA),
          strip.text.x          = element_text(size = 10, color = "black", face = "bold"),
          strip.background      = element_rect(fill = NA, color = NA),
          plot.title            = element_text(size = 12)
    )
} # set up plot theme

addbracket <- function(x){paste("[", x, "]")} # add brackets between words

# Set options in Rstan
set.seed(10)
rstan_options(auto_write = TRUE) # translate to STAN platform for running Bayesian model
options(mc.cores = parallel::detectCores()) # detects how many cores available to use

# Set directory
setwd('/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/') 

# Set file paths
data_path     <- file.path("data")
spatial_path  <- file.path("Spatial data")
rasterSp_path <- "rasterSp/SpeciesData/"
```


**Abbreviations**

- AI: Aridity Index
- EWL: Evaporative water loss
- IUCN: International Union for Conservation of Nature
- PDSI: Palmer Drought Severity Index
- PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
- RH: Relative humidity
- SPP: Shared Socioeconomic Pathway
- WU: water uptake

# Spatial risk {-}

## Descriptors {.tabset .tabset-fade .tabset-pills -}

The following series of code is used to estimate the risk of extant anurans to increasing aridity (Aridity Index; AI) and drought (Palmer Drought Severity Index; PDSI). The AI was categorised to five categories and The AI was categorised to five categories (Humid, Dry sub-humid, Semi-arid, Arid, and Hyper-arid) and the PDSI was categorised to seven categories (Extremely moist, Very moist, Moderate moist, Normal, Moderate drought, Severe drought, Extreme drought) based on descriptions from @Budyko1961 and @Palmer1965, respectively (**Table S1**).

Generalised ecological types or ‘ecotype’ were classified based on descriptions from @Moen2017 focusing on adult behaviour and microhabitat preferences outside the breeding season given that many anurans breed in water but are not adapted to live in water all year (**Table S2**).

### Table S1 {.tabset .tabset-fade .tabset-pills -} 

**Table S1** Aridity index [@Budyko1961] and the Palmer Drought Severity Index [@Palmer1965] categorised.

```{r table S1, echo=FALSE, message=FALSE}
data.frame(AI = c("Humid", "Dry sub-humid", "Semi-arid", "Arid","Hyper-arid", "", ""), 
           Value = c("≥ 0.65", "0.50 – ≤ 0.65", "0.20 – ≤ 0.50", "0.05 – ≤ 0.20", "< 0.05", "", ""),
           PDSI = c("Extremely moist", "Very moist", "Moderate moist", "Normal",
                                      "Moderate drought", "Severe drought", " Extreme drought"),
           Value = c("≥ 4", "3 – ≤ 4", "2 – ≤ 3", "-2 – ≤ 2", "-3 – ≤ -2", "-4 – ≤ -3", "< -4")) %>%
  dplyr::rename("Aridity index (AI)"                   = AI,
                "AI value"                             = Value,
                "Palmer Drought Severity Index (PDSI)" = PDSI,
                "PDSI Value"                           = Value.1) %>%
  knitr::kable() 
```

### Table S2 {.tabset .tabset-fade .tabset-pills -} 

**Table S2** Microhabitat preference or ecotype [@Moen2017].

```{r table S2, echo=FALSE, message=FALSE}
data.frame(Ecotype = c("Aquatic", "Arboreal", "Semi-aquatic ", "Ground-dwelling","Fossorial", "Stream-dwelling"), 
           Description = c("Almost always in water.", 
                           "Ability to climb vegetation and spends a substantial amount of time above ground (e.g. forests, on top of vegetations in wetlands).", 
                           "Stay near water bodies, usually permanent water (e.g. ground-level wetlands). Often observed in water and nearby vegetation.", 
                           "Terrestrial only, not always near water bodies, however always in a moist environment (e.g. forest leaf litter, moist soils, scrublands, grassland). Sometimes above ground and sometimes semi-fossorial. Can be classified as broad.", 
                           "The non-breeding season is spent underground in burrows. Capable of burrowing and aestivating.", 
                           "Restricted to near fast-flowing streams usually on rocks or vegetation near streams.")) %>%
  knitr::kable() 
```

## Species richness {-}

Anuran (frogs and toads)  distribution shape files were obtained from the International Union for Conservation of Nature's Red List of Threatened Species ([IUCN Red List](https://www.iucnredlist.org/resources/spatial-data-download); extracted on 11/01/2021). Species richness was defined as the sum of species in each grid cell (0.5°), based on the geographic range, and was calculated using the `rasterizeIUCN` and `calcSR` function from the from the [rasterSp](https://github.com/RS-eco/rasterSp) package. Ecotype-specific species richness was also calculated.

```{r sp, message=FALSE, cache=TRUE, results="hide"}
filedir <- "rasterSp/"

# Clean raw data
frog_dat <- read.csv(file.path(data_path, "anuran_species_list.csv")) %>%
  dplyr::select(genus:strategy, SVL_cm:binomial_tree_phylo) %>%
  dplyr::mutate(lnSVL       = log(SVL_cm),
                lnMass      = log(mass_g),
                lnArea      = log(shape_area),
                IUCN        = factor(IUCN, levels = c("Least Concern", "Near Threatened", "Vulnerable", "Endangered", "Critically Endangered", "Extinct")),
                order_name  = factor(order_name),
                family_name = factor(family_name),
                ecotype     = factor(ecotype)) %>%
  dplyr::filter(order_name == "ANURA" & ecotype != "" & IUCN != "" & IUCN != "Extinct") %>%
  droplevels()

# Convert shape files into rasters and save to file once (downloaded shape files 08/01/2020)
# only extracted distribution of native range
#anuran_rast <- rasterizeIUCN(dsn = paste0(filedir, "ANURA/ANURA.shp"), resolution = 0.5, seasonal = c(1, 2), origin = 1, presence = c(1,2), save = TRUE, path = paste0(rasterSp_path))

# Calculate anuran richness and by ecotype 
# The calcSR function uses a stepwise procedure to calculate the sum of species for each grid cell.
anuran_sr    <- rasterSp::calcSR(species_names = frog_dat$binomial_IUCN, 
                                 path = paste0(rasterSp_path))
aquatic_sr   <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Aquatic", "binomial_IUCN"], 
                                 path = paste0(rasterSp_path))
arboreal_sr  <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Arboreal", "binomial_IUCN"], 
                                 path = paste0(rasterSp_path))
fossorial_sr <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Fossorial", "binomial_IUCN"],
                                 path = paste0(rasterSp_path))
ground_sr    <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Ground-dwelling", "binomial_IUCN"], 
                                 path = paste0(rasterSp_path))
semi_aq_sr   <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Semi-aquatic", "binomial_IUCN"], 
                                 path = paste0(rasterSp_path))
stream_sr    <- rasterSp::calcSR(species_names = frog_dat[frog_dat$ecotype == "Stream-dwelling", "binomial_IUCN"], 
                                 path = paste0(rasterSp_path))

# Convert raster to matrix then to data frame
anuran_sr_df    <- raster::as.data.frame(raster::rasterToPoints(anuran_sr)) %>% dplyr::rename(species_n = layer)
aquatic_sr_df   <- raster::as.data.frame(raster::rasterToPoints(aquatic_sr)) %>% dplyr::rename(aquatic_n = layer)
arboreal_sr_df  <- raster::as.data.frame(raster::rasterToPoints(arboreal_sr)) %>% dplyr::rename(arboreal_n = layer)
fossorial_sr_df <- raster::as.data.frame(raster::rasterToPoints(fossorial_sr)) %>% dplyr::rename(fossorial_n = layer)
ground_sr_df    <- raster::as.data.frame(raster::rasterToPoints(ground_sr)) %>% dplyr::rename(ground_n = layer)
semi_aq_sr_df   <- raster::as.data.frame(raster::rasterToPoints(semi_aq_sr)) %>% dplyr::rename(semi_aq_n = layer)
stream_sr_df    <- raster::as.data.frame(raster::rasterToPoints(stream_sr)) %>% dplyr::rename(stream_n = layer)
```

There are `r length(unique(frog_dat$binomial_IUCN))` anuran species from the IUCN Red List used for the analysis.

## Calculate AI and PDSI {-}

High resolution (~4 km^2^) global dataset on precipitation (mm/month) and potential evapotranspiration (mm/month) were obtained from Abatzoglou et al. (2018) under 1) the current climate (1970–2000), 2) an intermediate greenhouse gas emission scenario of +2°C (Shared Socioeconomic Pathway 2–4.5; SPP2–4.5), and 3) a high greenhouse gas emission or “business-as-usual” scenario of +4°C (SSP5–8.5) by 2080–2099 (**Fig. S1a–f**). 

We obtained a self-calibrated PDSI with Penman–Monteith potential evapotranspiration representing the current climate (1970–2000) and an intermediate and high emission scenario by 2080–2099 (SSP2–4.5 and SSP5-8.5) from Zhao and Dai (2022). The SSP2–4.5 and SSP5–8.5 scenarios were based on the average of 25 CMIP6 models of precipitation, evapotranspiration, soil moisture, and runoff (Eyring et al., 2016), where the mean annual surface temperature is expected to increase by 2.7°C (2.1–3.5°C range) and 4.4°C (3.3–5.7°C range), respectively by 2080–2100 (IPCC, 2021).

```{r clim dat, message=FALSE, cache=TRUE, results="hide"}
# Import the downloaded files
ppt_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_ppt.nc')), anuran_sr) # Precipitation year 1981-2010 - Pr (mm)
pet_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_pet.nc')), anuran_sr) # Evapotranspiration 1981-2010 - ET0 (mm)

# +2C and +4C future scenarios 
ppt_2C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate2C_ppt.nc')), anuran_sr)
pet_2C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate2C_pet.nc')), anuran_sr)
ppt_4C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate4C_ppt.nc')), anuran_sr)
pet_4C_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate4C_pet.nc')), anuran_sr)

# Obtain mean values from 1981-2010
ppt_mean_rast    <- raster::calc(ppt_rast, fun = mean, na.rm = TRUE)
pet_mean_rast    <- raster::calc(pet_rast, fun = mean, na.rm = TRUE) 
ppt_2C_mean_rast <- raster::calc(ppt_2C_rast, fun = mean, na.rm = TRUE) 
pet_2C_mean_rast <- raster::calc(pet_2C_rast, fun = mean, na.rm = TRUE)
ppt_4C_mean_rast <- raster::calc(ppt_4C_rast, fun = mean, na.rm = TRUE)
pet_4C_mean_rast <- raster::calc(pet_4C_rast, fun = mean, na.rm = TRUE) 

# Obtain mean values from 10-year monthly PDSI
PDSI_ssp245_rast <- raster::stack(x = file.path(spatial_path,  'pdsisc.monthly.1900-2100.r2.5x2.5.EnsAvg25Models.TP2.ipe-2.ssp245.nc'))
PDSI_ssp585_rast <- raster::stack(x = file.path(spatial_path,  'pdsisc.monthly.1900-2100.r2.5x2.5.EnsAvg25Models.TP2.ipe-2.ssp585.nc'))

PDSI_cur_rast <- raster::subset(PDSI_ssp245_rast, grep("X197.*|X198.*|X199.*", names(PDSI_ssp245_rast), value = T))
PDSI_2C_rast  <- raster::subset(PDSI_ssp245_rast, grep("X207.*|X208.*|X209.*", names(PDSI_ssp245_rast), value = T))
PDSI_4C_rast  <- raster::subset(PDSI_ssp585_rast, grep("X207.*|X208.*|X209.*", names(PDSI_ssp585_rast), value = T))

PDSI_cur_mean_rast <- raster::calc(PDSI_cur_rast, fun = mean, na.rm = TRUE)
PDSI_2C_mean_rast  <- raster::calc(PDSI_2C_rast, fun = mean, na.rm = TRUE) 
PDSI_4C_mean_rast  <- raster::calc(PDSI_4C_rast, fun = mean, na.rm = TRUE) 

# Calculate aridity index [Precipitation (ppt) / Evapotranspiration (pet)]
ai_rast    <- raster::overlay(x = ppt_mean_rast, 
                              y = pet_mean_rast, 
                              fun = function(x, y) {return(x / y)})
ai_2C_rast <- raster::overlay(x = ppt_2C_mean_rast, 
                              y = pet_2C_mean_rast, 
                              fun = function(x, y) {return(x / y)}) 
ai_4C_rast <- raster::overlay(x = ppt_4C_mean_rast, 
                              y = pet_4C_mean_rast, 
                              fun = function(x, y) {return(x / y)}) 

# Calculate change in PDSI
# Reproject intensity raster to match anuran_sr
PDSI_cur_mean_rast <- raster::projectRaster(PDSI_cur_mean_rast, anuran_sr)
PDSI_2C_mean_rast  <- raster::projectRaster(PDSI_2C_mean_rast, anuran_sr)
PDSI_4C_mean_rast  <- raster::projectRaster(PDSI_4C_mean_rast, anuran_sr)

PDSI_2C_diff_rast <- raster::overlay(x = PDSI_cur_mean_rast, 
                                     y = PDSI_2C_mean_rast, 
                                     fun = function(x, y) {return(y - x)}) 
PDSI_4C_diff_rast <- raster::overlay(x = PDSI_cur_mean_rast, 
                                     y = PDSI_4C_mean_rast, 
                                     fun = function(x, y) {return(y - x)}) 

# Convert raster to matrix then to data frame
ai_df    <- raster::as.data.frame(raster::rasterToPoints(ai_rast))
ai_2C_df <- raster::as.data.frame(raster::rasterToPoints(ai_2C_rast))
ai_4C_df <- raster::as.data.frame(raster::rasterToPoints(ai_4C_rast))

PDSI_2C_diff_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_2C_diff_rast))
PDSI_4C_diff_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_4C_diff_rast))

# Convert aridity index into category
ai_df <- ai_df %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',             
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid',
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid', 
    layer >= 0.05 & layer < 0.2 ~ 'Arid', 
    layer < 0.05 ~ 'Hyper-arid'
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                              levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                         'Dry sub-humid', 'Humid'),
                              ordered = TRUE))

ai_2C_df <- ai_2C_df %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',                       
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid', 
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid',      
    layer >= 0.05 & layer < 0.2 ~ 'Arid',   
    layer < 0.05 ~ 'Hyper-arid'
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                           levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                      'Dry sub-humid', 'Humid'),
                           ordered = TRUE))

ai_4C_df <- ai_4C_df %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',                       
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid', 
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid',      
    layer >= 0.05 & layer < 0.2 ~ 'Arid',   
    layer < 0.05 ~ 'Hyper-arid'
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                           levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                      'Dry sub-humid', 'Humid'),
                           ordered = TRUE))

# Convert PDSI to category
PDSI_2C_diff_df <- PDSI_2C_diff_df %>% 
  # Recode
  dplyr::mutate(change_2C = case_when(
    layer >= 4 ~ "4",                
    layer >= 3 & layer < 4 ~ '3',
    layer >= 2 & layer < 3 ~ '2', 
    layer >= 1 & layer < 2 ~ '1',  
    layer >= -1 & layer < 1 ~ '0',  
    layer >= -2 & layer < -1 ~ '-1', 
    layer >= -3 & layer < -2 ~ '-2', 
    layer >= -4 & layer < -3 ~ '-3', 
    layer < -4 ~ '-4'
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(change_2C = factor(change_2C,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE))

PDSI_4C_diff_df <- PDSI_4C_diff_df %>% 
  # Recode
  dplyr::mutate(change_4C = case_when(
    layer >= 4 ~ "4",                
    layer >= 3 & layer < 4 ~ '3',
    layer >= 2 & layer < 3 ~ '2', 
    layer >= 1 & layer < 2 ~ '1',  
    layer >= -1 & layer < 1 ~ '0',  
    layer >= -2 & layer < -1 ~ '-1', 
    layer >= -3 & layer < -2 ~ '-2', 
    layer >= -4 & layer < -3 ~ '-3', 
    layer < -4 ~ '-4'
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(change_4C = factor(change_4C,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE))
```

```{r Fig S1, message=FALSE, fig.align='center', fig.height=6.5, fig.width=9}
# Crop and mask
world      <- rgdal::readOGR(file.path(spatial_path, "ne_50m_land/ne_50m_land.shp"))
world_spdf <- sp::SpatialPolygonsDataFrame(world, world@data) 

# Precipitation
ppt_curr_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650), name = "Precipitation (mm)") +
  ggtitle("Current (1981-2010)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  guides(fill = guide_colourbar(barwidth = 10, barheight = 0.3, 
                                label.position = "bottom")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.position = "bottom") +
  coord_fixed(ratio = 1)  

ppt_2C_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_2C_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650)) +
  ggtitle("+2°C (2080-2100)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)

ppt_4C_plot <- raster::as.data.frame(raster::rasterToPoints(ppt_4C_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "YlGnBu", lim = c(0, 650)) +
  ggtitle("+4°C (2080-2100)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)  

ppt_legend <- cowplot::get_legend(ppt_curr_plot)

ppt_prow <- cowplot::plot_grid(ppt_curr_plot + theme(legend.position = "none"), 
                   ppt_2C_plot +  theme(legend.position = "none"), 
                   ppt_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('a', 'b', 'c')) 

ppt_plots <- cowplot::plot_grid(ppt_prow, ppt_legend, ncol = 1, rel_heights = c(1, .1))

# Evapotranspiration
pet_curr_plot <- raster::as.data.frame(raster::rasterToPoints(pet_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300), name = "Evapotranspiration (mm)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  guides(fill = guide_colourbar(barwidth = 10, barheight = 0.3, 
                                label.position = "bottom")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.position = "bottom") +
  coord_fixed(ratio = 1)  

pet_2C_plot <- raster::as.data.frame(raster::rasterToPoints(pet_2C_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300)) +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)

pet_4C_plot <- raster::as.data.frame(raster::rasterToPoints(pet_4C_mean_rast)) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = layer)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "Rocket", lim = c(0, 300)) +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)  

pet_legend <- cowplot::get_legend(pet_curr_plot)

pet_prow <- cowplot::plot_grid(pet_curr_plot + theme(legend.position = "none"), 
                   pet_2C_plot +  theme(legend.position = "none"), 
                   pet_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('d', 'e', 'f')) 

pet_plots <- cowplot::plot_grid(pet_prow, pet_legend, ncol = 1, rel_heights = c(1, .1))

# Aridity Index
arid_col <- c('#8E063B', '#CB6D53', '#E99A2C', '#F5D579', 'white')
ai_curr_plot <- ggplot() +
  geom_raster(data = ai_df, aes(y = y, x = x, fill = category)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = arid_col,
                    guide = guide_legend(reverse = TRUE),
                    name = "Aridity Index") +
  ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + 
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.key.size = unit(0.2, 'cm'),
        legend.position = "bottom") +
  coord_fixed(ratio = 1)

ai_2C_plot <- ggplot() +
  geom_raster(data = ai_2C_df, aes(y = y, x = x, fill = category)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = arid_col,
                    guide = guide_legend(reverse = TRUE),
                    name = "Aridity Index") +
  ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + 
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)

ai_4C_plot <- ggplot() +
  geom_raster(data = ai_4C_df, aes(y = y, x = x, fill = category)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = arid_col,
                    guide = guide_legend(reverse = TRUE),
                    name = "Aridity Index") +
  ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme_void() + 
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)

ai_legend <- cowplot::get_legend(ai_curr_plot)

ai_prow <- cowplot::plot_grid(ai_curr_plot + theme(legend.position = "none"), 
                   ai_2C_plot +  theme(legend.position = "none"), 
                   ai_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('g', 'h', 'i')) 

ai_plots <- cowplot::plot_grid(ai_prow, ai_legend, ncol = 1, rel_heights = c(1, .1))

cowplot::plot_grid(ppt_plots, pet_plots, ai_plots, ncol = 1)
```

**Fig. S1.** Climate data used to calculate the aridity index. Mean precipitation (mm) for (**a**) the current scenario from 1981-2010, (**b**) under an intermediate emission scenario (Shared Socioeconomic Pathways 2 - 4.5; SSP2-4.5), and (**c**) under a high emission scenario (SSP5-8.5) by 2080-2100. Mean potential evapotranspiration (mm) for (**d**) the current scenario from 1981-2010, (**e**) under an intermediate emission scenario (SSP2-4.5), and (**f**) under a high emission scenario (SSP5-8.5) by 2080-2100. Calculated Aridity Index for (**g**) the current scenario from 1981-2010, (**h**) under an intermediate emission scenario (SSP2-4.5), and (**i**) under a high emession scenario (SSP5-8.5) by 2080-2100. 

## AI risk {-}

To examine the relationship of species richness with aridity, the number of species per gird cell was overlapped with the aridity raster layer, where each grid was assigned an AI category. The change in species richness between the current and projected (either +2 or +4 °C warming) AI category was calculated as the change in AI category grids (resolution 0.5° for decimal degree coordinates) occupied by anurans relative to the future projection. A decrease indicates reduced number of species with the assigned AI category and vice versa.

```{r AI}
# Merge aridity index, +4C and amphibian species richness
ai_sp_df <- ai_df %>% 
  dplyr::full_join(ai_2C_df, by = c("x" ,"y")) %>%
  dplyr::full_join(ai_4C_df, by = c("x" ,"y")) %>%
  dplyr::full_join(anuran_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>% 
  dplyr::rename(aridity = layer.x,
                aridity_2C = layer.y,
                aridity_4C = layer,
                category = category.x,
                category_2C = category.y,
                category_4C = category) %>%
  drop_na(category)

# Calculate grid cells occupied for current climate
ai_sp_sum <- data.frame(ai_sp_df %>% 
                          dplyr::group_by(category) %>% 
                          dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                           aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                           arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                           fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                           ground_n    = length(ground_n[!is.na(ground_n)]),
                                           semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                           stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(scenario = "Current",
                all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100)

# Calculate grid cells occupied for future climate
ai_2C_sp_sum <- data.frame(ai_sp_df %>% 
                             dplyr::group_by(category_2C) %>% 
                             dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                              aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                              arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                              fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                              ground_n    = length(ground_n[!is.na(ground_n)]),
                                              semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                              stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(scenario = "SSP245",
                all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>% 
  dplyr::rename(category = category_2C) 

ai_4C_sp_sum <- data.frame(ai_sp_df %>% 
                             dplyr::group_by(category_4C) %>% 
                             dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                              aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                              arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                              fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                              ground_n    = length(ground_n[!is.na(ground_n)]),
                                              semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                              stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(scenario = "SSP585",
                all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                rboreal_freq   = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>% 
  dplyr::rename(category = category_4C)
```

## PDSI risk {-}

With a monthly prediction of PDSI from 1950 to 2100 globally available from Zhao and Dai (2022), we classified future drought risk in three ways: 1) increase in drought intensity (magnitude change in PDSI), increase in drought frequency (monthly PDSI counts below -2 per year), increase in duration (number of consecutive months with PDSI values below -2). Change in drought intensity (ΔPDSI[intensity]), frequency (ΔPDSI[frequency]),  and duration (ΔPDSI[duration]) under a +2 or +4 °C warming scenario (2080–2100) was calculated relative to the 1970–2000 monthly climatology per gird cell (ΔPDSI = PDSI[future] – PDSI[current]). Note, the temporal resolution was limited to monthly variation as the PDSI was developed to monitor long-term meteorological drought.

```{r PDSI int}
# Change in PDSI intensity 
PDSI_sp_df <- anuran_sr_df %>% 
  dplyr::full_join(PDSI_2C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(PDSI_4C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>% 
  dplyr::rename(PDSI_2C = layer.x,
                PDSI_4C = layer.y) %>%
  drop_na(change_2C) %>%
  filter(species_n != "NA")

# Calculate grid cells occupied for current climate
PDSI_sp_2C <- data.frame(PDSI_sp_df %>% 
                           dplyr::group_by(change_2C) %>% 
                           dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                            aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                            arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                            fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                            ground_n    = length(ground_n[!is.na(ground_n)]),
                                            semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                            stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100)

PDSI_sp_4C <- data.frame(PDSI_sp_df %>% 
                           dplyr::group_by(change_4C) %>% 
                           dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                            aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                            arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                            fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                            ground_n    = length(ground_n[!is.na(ground_n)]),
                                            semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                            stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100)
```

```{r PDSI freq}
# Calculate frequency of moderate to extreme drought (<-2 PDSI) per year.

# Temporal change in PDSI intensity
PDSI_2C_time_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ssp245_rast))
PDSI_4C_time_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ssp585_rast))

# Convert wide to long
freq_2C_df <- PDSI_2C_time_df %>%
  tidyr::pivot_longer(!c("x","y"), names_to = "dates", values_to = "PDSI") %>%
  data.frame() %>%
  tidyr::separate(dates, c("year", 'months')) %>%
  dplyr::mutate(year = as.numeric(substring(year, 2))) %>%
  dplyr::filter(year >= 1950 & year != 2100)

freq_4C_df <- PDSI_4C_time_df %>%
  tidyr::pivot_longer(!c("x","y"), names_to = "dates", values_to = "PDSI") %>%
  data.frame() %>%
  tidyr::separate(dates, c("year", 'months')) %>%
  dplyr::mutate(year = as.numeric(substring(year, 2))) %>%
  dplyr::filter(year >= 1950 & year != 2100)

# Summarise average monthly counts <-2 PDSI from 1970 to 1999 or 2080 to 2100
freq_mean_df <- freq_2C_df %>%
  dplyr::filter(year >= 1970 & year <= 1999) %>%
  dplyr::group_by(x, y, year) %>%
  dplyr::summarise(count = sum(PDSI < -2)) %>%
  dplyr::group_by(x, y) %>%
  dplyr::summarise(mean_curr = mean(count))
  
freq_2C_diff_df <- freq_2C_df %>%
  dplyr::filter(year >= 2080 & year != 2100) %>%
  dplyr::group_by(x, y, year) %>%
  dplyr::summarise(count = sum(PDSI < -2)) %>% 
  dplyr::group_by(x, y) %>%
  dplyr::summarise(mean_2C = mean(count)) %>% 
  dplyr::inner_join(freq_mean_df, by = c("x" = "x", "y" = "y")) %>%
  dplyr::mutate(diff_2C = mean_2C - mean_curr)

freq_4C_diff_df <- freq_4C_df %>%
  dplyr::filter(year >= 2080 & year != 2100) %>%
  dplyr::group_by(x, y, year) %>%
  dplyr::summarise(count = sum(PDSI < -2)) %>% 
  dplyr::group_by(x, y) %>%
  dplyr::summarise(mean_4C = mean(count)) %>% 
  dplyr::inner_join(freq_2C_diff_df, by = c("x" = "x", "y" = "y")) %>%
  dplyr::mutate(diff_4C = mean_4C - mean_curr)
```

```{r PDSI dur}
# Calculate duration of moderate to extreme drought (<-2 PDSI) from 1970 to 1999
dur_curr_df <- freq_2C_df %>%
  dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>%
  dplyr::filter(year >= 1970 & year <= 1999) %>%
  dplyr::group_by(x, y) %>% 
  dplyr::summarise(mean_curr = mean(rle(bin)$lengths[rle(bin)$values==1])) %>%
  replace(is.na(.), 0)

# Calculate duration of moderate to extreme drought (<-2 PDSI) from 2080 to 2100
dur_2C_df <- freq_2C_df %>%
  dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>% 
  dplyr::filter(year >= 2080 & year != 2100) %>%
  dplyr::group_by(x, y) %>% 
  dplyr::summarise(mean_2C = mean(rle(bin)$lengths[rle(bin)$values==1])) %>%
  replace(is.na(.), 0) %>%
  dplyr::inner_join(dur_curr_df, by = c("x" = "x", "y" = "y")) %>%
  dplyr::mutate(diff_2C = mean_2C - mean_curr)

dur_4C_df <- freq_4C_df %>% 
  dplyr::mutate(bin = ifelse(PDSI < -2, 1, 0)) %>%
  dplyr::group_by(x, y) %>% 
  dplyr::filter(year >= 2080 & year != 2100) %>%
  dplyr::summarise(mean_4C = mean(rle(bin)$lengths[rle(bin)$values==1])) %>%
  replace(is.na(.), 0) %>%
  dplyr::inner_join(dur_2C_df, by = c("x" = "x", "y" = "y")) %>%
  dplyr::mutate(diff_4C = mean_4C - mean_curr)
```

The simultaneous risk of drought intensity, frequency, and duration within a grid cell that are occupied by anurans was calculated by converting each risk category as binary. Gird cells with a ΔPDSI[intensity] below -1 (indicating a decrease in PDSI) were assigned a ‘1’ binary. Both ΔPDSI[frequency] and ΔPDSI[duration] were assigned a binary of ‘1’ if the grid cell has a value of 1 month or higher (indicating increase in frequency or duration relative to current scenario). The number of overlapping binaries were summed up per grid cell. Therefore, a risk factor of 2 indicate anurans in the assigned grid cell are at increasing risk of two drought events. We estimated which species assemblages were risk to drought using an arbitrary risk factor (species richness × drought risk), where grid cells with high drought risk and high species richness have higher species-assemblage risk than gird cells with high drought risk and low species richness (low species-assemblage risk).

```{r PDSI comb}
# Intensity
names(PDSI_2C_diff_rast) <- "delta_int_2C"
names(PDSI_4C_diff_rast) <- "delta_int_4C"

# Frequency
freq_diff_rast <- rasterFromXYZ(freq_4C_diff_df) # convert to raster
crs(freq_diff_rast) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
freq_diff_rast <- projectRaster(freq_diff_rast, anuran_sr) # match anuran extent
freq_diff_df   <- raster::as.data.frame(raster::rasterToPoints(freq_diff_rast))

PDSI_freq_diff <- subset(freq_diff_rast, 4:5)
names(PDSI_freq_diff) <- c("delta_freq_2C", "delta_freq_4C")

# Duration
dur_diff_rast <- raster::rasterFromXYZ(dur_4C_df) # convert to raster
crs(dur_diff_rast) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
dur_diff_rast <- raster::projectRaster(dur_diff_rast, anuran_sr) # match anuran extent
dur_diff_df <- raster::as.data.frame(raster::rasterToPoints(dur_diff_rast))

PDSI_dur_diff  <- subset(dur_diff_rast, 4:5)
names(PDSI_dur_diff) <- c("delta_dur_2C", "delta_dur_4C")

# Combine relative PDSI metrics
PDSI_risk_comb_rast <- raster::stack(PDSI_2C_diff_rast, PDSI_4C_diff_rast, PDSI_freq_diff, PDSI_dur_diff, resample(anuran_sr, PDSI_4C_diff_rast))
                             
PDSI_risk_comb_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_risk_comb_rast)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(delta_int_2C_bin = ifelse(delta_int_2C < -1, 1, 0), # delta_PDSI < -1
         delta_int_4C_bin = ifelse(delta_int_4C < -1, 1, 0), # delta_PDSI < -1
         delta_freq_2C_bin = ifelse(delta_freq_2C >1, 1, 0), # month > 1
         delta_freq_4C_bin = ifelse(delta_freq_4C >1, 1, 0), # month > 1
         delta_dur_2C_bin = ifelse(delta_dur_2C >1, 1, 0), # month > 1
         delta_dur_4C_bin = ifelse(delta_dur_4C >1, 1, 0)) %>% # month > 1 
  dplyr::rowwise() %>%
  dplyr::mutate(count_2C = sum(delta_int_2C_bin, delta_freq_2C_bin, delta_dur_2C_bin, na.rm = T),
         
         count_4C = sum(delta_int_4C_bin, delta_freq_4C_bin, delta_dur_4C_bin, na.rm = T),
         risk_2C  = sp_n * count_2C,
         risk_4C  = sp_n * count_4C,
         count_2C = factor(count_2C, levels = c("3", "2", "1")),
         count_4C = factor(count_4C, levels = c("3", "2", "1"))
         )
```

```{r Fig S2, message=FALSE, fig.align='center', fig.height=6.5, fig.width=9}
# Combine absolute PDSI metrics
PDSI_ab_rast <- raster::stack(PDSI_cur_mean_rast, PDSI_2C_mean_rast, PDSI_4C_mean_rast, freq_diff_rast, dur_diff_rast, resample(anuran_sr, PDSI_4C_mean_rast)) 
PDSI_ab_rast_crop <- raster::mask(crop(PDSI_ab_rast, extent(world)), world) # crop 

PDSI_ab_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_ab_rast_crop)) %>%
  dplyr::rename(PDSI_cur = "layer.1",
                PDSI_2C  = "layer.2",
                PDSI_4C  = "layer.3",
                freq_cur = "mean_curr.1",
                freq_2C  = "mean_2C.1",
                freq_4C  = "mean_4C.1",
                dur_cur  = "mean_curr.2",
                dur_2C   = "mean_2C.2",
                dur_4C   = "mean_4C.2",
                sp_n     = "layer.4") %>%
  filter(sp_n != "NA")

colours_PDSI <- RColorBrewer::brewer.pal(9, "RdBu")

# Intensity
PDSI_cur_plot <- PDSI_ab_df %>%
  dplyr::mutate(PDSI_cur_cat = case_when(
    PDSI_cur >= 2 & PDSI_cur < 3 ~ '2',  
    PDSI_cur >= 1 & PDSI_cur < 2 ~ '1',  
    PDSI_cur >= -1 & PDSI_cur < 1 ~ '0'
  )) %>% 
  dplyr::mutate(PDSI_cur_cat = factor(PDSI_cur_cat,
                                levels = c('0', '1', '2'),
                                ordered = TRUE)) %>%
  filter(PDSI_cur_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = PDSI_cur_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#F7F7F7", "#D1E5F0" ,"#92C5DE")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean PDSI intensity (1970–2000)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

PDSI_2C_plot <- PDSI_ab_df %>%
  dplyr::mutate(PDSI_2C_cat = case_when(
    PDSI_2C >= 4 ~ "4", 
    PDSI_2C >= 3 & PDSI_2C < 4 ~ '3',
    PDSI_2C >= 2 & PDSI_2C < 3 ~ '2',  
    PDSI_2C >= 1 & PDSI_2C < 2 ~ '1',  
    PDSI_2C >= -1 & PDSI_2C < 1 ~ '0',
    PDSI_2C >= -2 & PDSI_2C < -1 ~ '-1',
    PDSI_2C >= -3 & PDSI_2C < -2 ~ '-2',
    PDSI_2C >= -4 & PDSI_2C < -3 ~ '-3',   
    PDSI_2C < -4 ~ '-4' 
  )) %>% 
  dplyr::mutate(PDSI_2C_cat = factor(PDSI_2C_cat,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE)) %>%
  filter(PDSI_2C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = PDSI_2C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = colours_PDSI) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean PDSI intensity (+2°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

PDSI_4C_plot <- PDSI_ab_df %>%
  dplyr::mutate(PDSI_4C_cat = case_when(
    PDSI_4C >= 4 ~ "4", 
    PDSI_4C >= 3 & PDSI_4C < 4 ~ '3',
    PDSI_4C >= 2 & PDSI_4C < 3 ~ '2',  
    PDSI_4C >= 1 & PDSI_4C < 2 ~ '1',  
    PDSI_4C >= -1 & PDSI_4C < 1 ~ '0',
    PDSI_4C >= -2 & PDSI_4C < -1 ~ '-1',
    PDSI_4C >= -3 & PDSI_4C < -2 ~ '-2',
    PDSI_4C >= -4 & PDSI_4C < -3 ~ '-3',   
    PDSI_4C < -4 ~ '-4' 
  )) %>% 
  dplyr::mutate(PDSI_4C_cat = factor(PDSI_4C_cat,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE)) %>%
  filter(PDSI_4C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = PDSI_4C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = colours_PDSI, name = "PDSI", guide = guide_legend(reverse = TRUE, nrow = 1)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean PDSI intensity (+4°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.key.height= unit(0.2, 'cm'),
        legend.key.width= unit(0.2, 'cm'),
        legend.position = "bottom") +
  coord_fixed(ratio = 1) # fixed ratio

int_legend <- cowplot::get_legend(PDSI_4C_plot)

int_prow <- cowplot::plot_grid(PDSI_cur_plot + theme(legend.position = "none"), 
                   PDSI_2C_plot +  theme(legend.position = "none"), 
                   PDSI_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('a', 'b', 'c')) 

int_plots <- cowplot::plot_grid(int_prow, int_legend, ncol = 1, rel_heights = c(1, .1))

# Frequency
freq_cur_plot <- PDSI_ab_df %>%
  dplyr::mutate(freq_cur_cat = case_when(
    freq_cur > 0.1 & freq_cur < 1 ~ '<1'
  )) %>%
  filter(freq_cur_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = freq_cur_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = "#ededed") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought frequency (1970–2000)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

freq_2C_plot <- PDSI_ab_df %>%
  dplyr::mutate(freq_2C_cat = case_when(
    freq_2C >= 10 ~ "10-12", 
    freq_2C >= 8 & freq_2C < 10 ~ '8-10',
    freq_2C >= 6 & freq_2C < 8 ~ '6-8',  
    freq_2C >= 4 & freq_2C < 6 ~ '4-6',  
    freq_2C >= 2 & freq_2C < 4 ~ '2-4',
    freq_2C >= 1 & freq_2C < 2 ~ '1-2',
    freq_2C > 0.1 & freq_2C < 1 ~ '<1'
  )) %>% 
  dplyr::mutate(freq_2C_cat = factor(freq_2C_cat,
                                     levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '<1'),
                                     ordered = TRUE)) %>%
  filter(freq_2C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = freq_2C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought frequency (+2°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

freq_4C_plot <- PDSI_ab_df %>%
  dplyr::mutate(freq_4C_cat = case_when(
    freq_4C >= 10 ~ "10-12", 
    freq_4C >= 8 & freq_4C < 10 ~ '8-10',
    freq_4C >= 6 & freq_4C < 8 ~ '6-8',  
    freq_4C >= 4 & freq_4C < 6 ~ '4-6',  
    freq_4C >= 2 & freq_4C < 4 ~ '2-4',
    freq_4C >= 1 & freq_4C < 2 ~ '1-2',
    freq_4C > 0.1 & freq_4C < 1 ~ '<1'
  )) %>% 
  dplyr::mutate(freq_4C_cat = factor(freq_4C_cat,
                                     levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '<1'),
                                     ordered = TRUE)) %>%
  filter(freq_4C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = freq_4C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7"), name = "Months", guide = guide_legend(reverse = TRUE, nrow = 1)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought frequency (+4°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.key.height= unit(0.2, 'cm'),
        legend.key.width= unit(0.2, 'cm'),
        legend.position = "bottom") +
  coord_fixed(ratio = 1) # fixed ratio

freq_legend <- cowplot::get_legend(freq_4C_plot)

freq_prow <- cowplot::plot_grid(freq_cur_plot + theme(legend.position = "none"), 
                   freq_2C_plot +  theme(legend.position = "none"), 
                   freq_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('d', 'e', 'f')) 

freq_plots <- cowplot::plot_grid(freq_prow, freq_legend, ncol = 1, rel_heights = c(1, .1))


# Duration
dur_cur_plot <- PDSI_ab_df %>%
  dplyr::mutate(dur_cur_cat = case_when(
    dur_cur >= 2 & dur_cur < 4 ~ '2-4',
     dur_cur >= 1 & dur_cur < 2 ~ '1-2',
    dur_cur > 0.1 & dur_cur < 1 ~ '<1'
  )) %>% 
  dplyr::mutate(dur_cur_cat = factor(dur_cur_cat,
                                levels = c('2-4', '1-2', '<1'),
                                ordered = TRUE)) %>%
  filter(dur_cur_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = dur_cur_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#FDDBC7", "#FAE9DF", "#F7F7F7")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought duration (1970–2000)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

dur_2C_plot <- PDSI_ab_df %>%
  dplyr::mutate(dur_2C_cat = case_when(
    dur_2C >= 10 ~ ">10", 
    dur_2C >= 8 & dur_2C < 10 ~ '8-10',
    dur_2C >= 6 & dur_2C < 8 ~ '6-8',  
    dur_2C >= 4 & dur_2C < 6 ~ '4-6',  
    dur_2C >= 2 & dur_2C < 4 ~ '2-4',
    dur_2C >= 1 & dur_2C < 2 ~ '1-2',
    dur_2C > 0.1 & dur_2C < 1 ~ '<1'
  )) %>% 
  dplyr::mutate(dur_2C_cat = factor(dur_2C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', '<1'),
                                ordered = TRUE))%>%
  filter(dur_2C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = dur_2C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought duration (+2°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

dur_4C_plot <- PDSI_ab_df %>%
  dplyr::mutate(dur_4C_cat = case_when(
    dur_4C >= 10 ~ ">10", 
    dur_4C >= 8 & dur_4C < 10 ~ '8-10',
    dur_4C >= 6 & dur_4C < 8 ~ '6-8',  
    dur_4C >= 4 & dur_4C < 6 ~ '4-6',  
    dur_4C >= 2 & dur_4C < 4 ~ '2-4',
    dur_4C >= 1 & dur_4C < 4 ~ '1-2',
    dur_4C > 0.1 & dur_4C < 1 ~ '<1'
  )) %>% 
  dplyr::mutate(dur_4C_cat = factor(dur_4C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', '<1'),
                                ordered = TRUE)) %>%
  filter(dur_4C_cat != "NA") %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = dur_4C_cat)) +
  geom_polygon(data = world_spdf, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7"), name = "Months", guide = guide_legend(reverse = TRUE, nrow = 1)) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Mean drought duration (+4°C)") +
  scale_y_continuous(limits = c(-60, 90), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-180, 180), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.key.size = unit(0.2, 'cm'),
        legend.position = "bottom") +
  coord_fixed(ratio = 1) # fixed ratio

dur_legend <- cowplot::get_legend(dur_4C_plot)

dur_prow <- cowplot::plot_grid(dur_cur_plot + theme(legend.position = "none"), 
                   dur_2C_plot +  theme(legend.position = "none"), 
                   dur_4C_plot + theme(legend.position = "none"), 
                   align = "v", ncol = 3, labels = c('g', 'h', 'i')) 

dur_plots <- cowplot::plot_grid(dur_prow, dur_legend, ncol = 1, rel_heights = c(1, .1))


cowplot::plot_grid(int_plots, freq_plots, dur_plots, ncol = 1)
```

**Fig. S2.** Mean monthly Palmer Drought Severity Index (PDSI) self-calibrated with Penman–Monteith potential evapotranspiration from (**a**) 1970–2000, and from 2080-2100 under (**b**) under an intermediate emission scenario (Shared Socioeconomic Pathways 2 - 4.5; SSP2-4.5), and (**c**) under a high emission scenario (SSP5-8.5). Mean yearly frequency of moderate to extreme drought (PDSI < -2) from (**d**) 1970–2000, and from 2080-2100 (**e**) under an intermediate emission scenario (SSP2-4.5), and (**f**) under a high emission scenario (SSP5-8.5). Mean duration of moderate to extreme drought (PDSI < -2) from (**g**) 1970–2000, and from 2080-2100 (**h**) under an intermediate emission scenario (SSP2-4.5), and (**i**) under a high emission scenario (SSP5-8.5).

## Summary {.tabset .tabset-fade .tabset-pills -}

### Ecotype AI distribution {.tabset .tabset-fade .tabset-pills -} 

Mean ± s.d. aridity index occupied by different anuran ecotypes.

```{r ecotype, echo=FALSE, message=FALSE}
ai_sp_df %>%
  dplyr::filter(aridity <=3) %>%
  dplyr::select(aridity, species_n:stream_n) %>%
  tidyr::pivot_longer(!aridity, names_to = "ecotype", values_to = "mean") %>%
  dplyr::filter(ecotype != "species_n") %>%
  drop_na(mean) %>%
  dplyr::group_by(ecotype) %>%
  dplyr::summarise(mean = mean(aridity),
                   sd   = sd(aridity)) %>%
  dplyr::mutate(ecotype = substr(ecotype,1,nchar(ecotype)-2),
                mean    = format(round(mean, 2), nsmall = 2),
                sd      = format(round(sd, 2), nsmall = 2),
                aridity = paste(mean, sd, sep = " ± "),
                ecotype = case_when(
    ecotype == "stream"    ~ "Stream-dwelling",
    ecotype == "arboreal"  ~ "Arboreal",
    ecotype == "semi_aq"  ~ "Semi-aquatic",
    ecotype == "aquatic"  ~ "Aquatic",
    ecotype == "ground"  ~ "Ground-dwelling",
    ecotype == "fossorial" ~ "Fossorial")) %>%
  dplyr::select(ecotype, aridity) %>%
  dplyr::arrange(desc(aridity)) %>%
  knitr::kable(col.names = c("Ecotype", "AI (mean ± s.d.)"))
```


### 2°C AI risk {.tabset .tabset-fade .tabset-pills -} 

Percent change in aridity by 2080–2100 (+ 2°C) relative to current climate (1981–2010) for all anurans and by ecotype.

```{r ai sum 2, echo=FALSE, message=FALSE}
# calculate relative change with future scenario
ai_sp_sum %>%
  dplyr::mutate(all_change       = (ai_2C_sp_sum$species_n - species_n) / species_n * 100,
                aquatic_change   = (ai_2C_sp_sum$aquatic_n - aquatic_n) / aquatic_n * 100,
                arboreal_change  = (ai_2C_sp_sum$arboreal_n - arboreal_n) / arboreal_n * 100,
                fossorial_change = (ai_2C_sp_sum$fossorial_n - fossorial_n) / fossorial_n * 100,
                ground_change    = (ai_2C_sp_sum$ground_n - ground_n) / ground_n * 100,
                semi_aq_change   = (ai_2C_sp_sum$semi_aq_n - semi_aq_n) / semi_aq_n * 100,
                stream_change    = (ai_2C_sp_sum$stream_n - stream_n) / stream_n * 100) %>%
  dplyr::select(c(category, all_change:stream_change)) %>%
  knitr::kable(col.names = c("Ecotype", 
                             "All (%)",
                             "Aquatic 2°C (%)", 
                             "Arboreal 2°C (%)", 
                             "Fossorial 2°C(%)", 
                             "Ground-dwelling 2°C (%)", 
                             "Semi-aquatic 2°C (%)", 
                             "Stream-dwelling 2°C (%)"),
                digits = 2) # 2 decimal places
```

### 4°C AI risk {.tabset .tabset-fade .tabset-pills -} 

Percent change in aridity by 2080–2100 (+ 4°C) relative to current climate (1981–2010) for all anurans and by ecotype.

```{r ai sum 4, echo=FALSE, message=FALSE}
# calculate relative change with future scenario
ai_sp_sum %>%
  dplyr::mutate(all_change       = (ai_4C_sp_sum$species_n - species_n) / species_n * 100,
                aquatic_change   = (ai_4C_sp_sum$aquatic_n - aquatic_n) / aquatic_n * 100,
                arboreal_change  = (ai_4C_sp_sum$arboreal_n - arboreal_n) / arboreal_n * 100,
                fossorial_change = (ai_4C_sp_sum$fossorial_n - fossorial_n) / fossorial_n * 100,
                ground_change    = (ai_4C_sp_sum$ground_n - ground_n) / ground_n * 100,
                semi_aq_change   = (ai_4C_sp_sum$semi_aq_n - semi_aq_n) / semi_aq_n * 100,
                stream_change    = (ai_4C_sp_sum$stream_n - stream_n) / stream_n * 100) %>%
  dplyr::select(c(category, all_change:stream_change)) %>%
  knitr::kable(col.names = c("Ecotype", 
                             "All (%)",
                             "Aquatic 2°C (%)", 
                             "Arboreal 2°C (%)", 
                             "Fossorial 2°C(%)", 
                             "Ground-dwelling 2°C (%)", 
                             "Semi-aquatic 2°C (%)", 
                             "Stream-dwelling 2°C (%)"),
                digits = 2) # 2 decimal places
```


### 2°C PDSI  risk {.tabset .tabset-fade .tabset-pills -} 

Percent change in drought intensity (ΔPDSI), frequency (ΔFrequency), and duration (ΔDuration) by 2080–2099 (+ 2°C scenario) relative to current climate (1970–2000) in grid cells occupied by anurans and by ecotype.

```{r PDSI sum 2, echo=FALSE, message=FALSE}
#Intensity
PDSI_sp_2C %>%
  dplyr::select(c(change_2C, all_freq:stream_freq)) %>%
  knitr::kable(col.names = c("ΔPDSI", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places

# Frequency
freq_2C_diff_df <- freq_diff_df %>%
  dplyr::select(x, y, diff_2C) %>%
  dplyr::mutate(diff_2C_cat = case_when(
    diff_2C >= 10 ~ "10-12", 
    diff_2C >= 8 & diff_2C < 10 ~ '8-10',
    diff_2C >= 6 & diff_2C < 8 ~ '6-8',  
    diff_2C >= 4 & diff_2C < 6 ~ '4-6',  
    diff_2C >= 2 & diff_2C < 4 ~ '2-4',
    diff_2C >= 1 & diff_2C < 2 ~ '1-2',
    diff_2C > 0 & diff_2C < 1 ~ '0-1',
    diff_2C >= -1 & diff_2C < 0 ~ '-0-1',
    diff_2C >= -2 & diff_2C < -1 ~ '-1-2',
    diff_2C >= -4 & diff_2C < -2 ~ '-2-4'
  )) %>% 
  dplyr::mutate(diff_2C_cat = factor(diff_2C_cat,
                                levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1', '-0-1', '-1-2', '-2-4'),
                                ordered = TRUE))

freq_sp_2C_df <- anuran_sr_df %>% 
  dplyr::full_join(freq_2C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>%
  filter(diff_2C_cat != "NA" & species_n != "NA")

data.frame(freq_sp_2C_df %>% 
                            dplyr::group_by(diff_2C_cat) %>% 
                            dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                             aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                             arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                             fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                             ground_n    = length(ground_n[!is.na(ground_n)]),
                                             semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                             stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>%
  dplyr::select(c(diff_2C_cat, all_freq:stream_freq)) %>%
   knitr::kable(col.names = c("ΔFrequency (months)", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places

# Duration
dur_2C_diff_df <- dur_diff_df %>%
  dplyr::select(x, y, diff_2C) %>%
  dplyr::mutate(diff_2C_cat = case_when(
    diff_2C >= 10 ~ ">10", 
    diff_2C >= 8 & diff_2C < 10 ~ '8-10',
    diff_2C >= 6 & diff_2C < 8 ~ '6-8',  
    diff_2C >= 4 & diff_2C < 6 ~ '4-6',  
    diff_2C >= 2 & diff_2C < 4 ~ '2-4',
    diff_2C >= 1 & diff_2C < 2 ~ '1-2',
    diff_2C > 0 & diff_2C < 1 ~ '0-1',
    diff_2C >= -1 & diff_2C < 0 ~ '-0-1',
    diff_2C >= -2 & diff_2C < -1 ~ '-1-2',
    diff_2C >= -4 & diff_2C < -2 ~ '-2-4',
    diff_2C >= -6 & diff_2C < -4 ~ '-4-6',
    diff_2C >= -8 & diff_2C < -6 ~ '-6-8',
    diff_2C >= -10 & diff_2C < -8 ~ '-8-10',   
    diff_2C < -10 ~ '<-10' 
  )) %>% 
  dplyr::mutate(diff_2C_cat = factor(diff_2C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1', '-0-1', '-1-2', '-2-4', '-4-6', '-6-8', '-8-10', '<-10'),
                                ordered = TRUE))

dur_sp_2C_df <- anuran_sr_df %>% 
  dplyr::full_join(dur_2C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>%
  filter(diff_2C_cat != "NA" & species_n != "NA")

data.frame(dur_sp_2C_df %>% 
                            dplyr::group_by(diff_2C_cat) %>% 
                            dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                             aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                             arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                             fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                             ground_n    = length(ground_n[!is.na(ground_n)]),
                                             semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                             stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>%
  dplyr::select(c(diff_2C_cat, all_freq:stream_freq)) %>%
   knitr::kable(col.names = c("ΔDuration (months)", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places
```

### 4°C PDSI risk {.tabset .tabset-fade .tabset-pills -} 

Percent change in drought intensity (ΔPDSI), frequency (ΔFrequency), and duration (ΔDuration) by 2080–2099 (+ 4°C scenario) relative to current climate (1970–2000) in grid cells occupied by anurans and by ecotype.

```{r PDSI sum 4, echo=FALSE, message=FALSE}
#Intensity
PDSI_sp_4C %>%
  dplyr::select(c(change_4C, all_freq:stream_freq)) %>%
  knitr::kable(col.names = c("ΔPDSI", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places

# Frequency
freq_4C_diff_df <- freq_diff_df %>%
  dplyr::select(x, y, diff_4C) %>%
  dplyr::mutate(diff_4C_cat = case_when(
    diff_4C >= 10 ~ "10-12", 
    diff_4C >= 8 & diff_4C < 10 ~ '8-10',
    diff_4C >= 6 & diff_4C < 8 ~ '6-8',  
    diff_4C >= 4 & diff_4C < 6 ~ '4-6',  
    diff_4C >= 2 & diff_4C < 4 ~ '2-4',
    diff_4C >= 1 & diff_4C < 2 ~ '1-2',
    diff_4C > 0 & diff_4C < 1 ~ '0-1',
    diff_4C >= -1 & diff_4C < 0 ~ '-0-1',
    diff_4C >= -2 & diff_4C < -1 ~ '-1-2',
    diff_4C >= -4 & diff_4C < -2 ~ '-2-4'
  )) %>% 
  dplyr::mutate(diff_4C_cat = factor(diff_4C_cat,
                                levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1', '-0-1', '-1-2', '-2-4'),
                                ordered = TRUE))

freq_sp_4C_df <- anuran_sr_df %>% 
  dplyr::full_join(freq_4C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>%
  filter(diff_4C_cat != "NA" & species_n != "NA")

data.frame(freq_sp_4C_df %>% 
                            dplyr::group_by(diff_4C_cat) %>% 
                            dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                             aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                             arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                             fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                             ground_n    = length(ground_n[!is.na(ground_n)]),
                                             semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                             stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>%
  dplyr::select(c(diff_4C_cat, all_freq:stream_freq)) %>%
   knitr::kable(col.names = c("ΔFrequency (months)", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places

# Duration
dur_4C_diff_df <- dur_diff_df %>%
  dplyr::select(x, y, diff_4C) %>%
  dplyr::mutate(diff_4C_cat = case_when(
    diff_4C >= 10 ~ ">10", 
    diff_4C >= 8 & diff_4C < 10 ~ '8-10',
    diff_4C >= 6 & diff_4C < 8 ~ '6-8',  
    diff_4C >= 4 & diff_4C < 6 ~ '4-6',  
    diff_4C >= 2 & diff_4C < 4 ~ '2-4',
    diff_4C >= 1 & diff_4C < 2 ~ '1-2',
    diff_4C > 0 & diff_4C < 1 ~ '0-1',
    diff_4C >= -1 & diff_4C < 0 ~ '-0-1',
    diff_4C >= -2 & diff_4C < -1 ~ '-1-2',
    diff_4C >= -4 & diff_4C < -2 ~ '-2-4',
    diff_4C >= -6 & diff_4C < -4 ~ '-4-6',
    diff_4C >= -8 & diff_4C < -6 ~ '-6-8',
    diff_4C >= -10 & diff_4C < -8 ~ '-8-10',   
    diff_4C < -10 ~ '<-10' 
  )) %>% 
  dplyr::mutate(diff_4C_cat = factor(diff_4C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1', '-0-1', '-1-2', '-2-4', '-4-6', '-6-8', '-8-10', '<-10'),
                                ordered = TRUE))

dur_sp_4C_df <- anuran_sr_df %>% 
  dplyr::full_join(dur_4C_diff_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(aquatic_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(arboreal_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(fossorial_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(ground_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(semi_aq_sr_df, by = c("x" ,"y")) %>% 
  dplyr::full_join(stream_sr_df, by = c("x" ,"y")) %>%
  filter(diff_4C_cat != "NA" & species_n != "NA")

data.frame(dur_sp_4C_df %>% 
                            dplyr::group_by(diff_4C_cat) %>% 
                            dplyr::summarise(species_n   = length(species_n[!is.na(species_n)]),
                                             aquatic_n   = length(aquatic_n[!is.na(aquatic_n)]),
                                             arboreal_n  = length(arboreal_n[!is.na(arboreal_n)]),
                                             fossorial_n = length(fossorial_n[!is.na(fossorial_n)]),
                                             ground_n    = length(ground_n[!is.na(ground_n)]),
                                             semi_aq_n   = length(semi_aq_n[!is.na(semi_aq_n)]),
                                             stream_n    = length(stream_n[!is.na(stream_n)]))) %>%
  dplyr::mutate(all_freq       = species_n / sum(species_n) * 100,
                aquatic_freq   = aquatic_n / sum(aquatic_n) * 100,
                arboreal_freq  = arboreal_n / sum(arboreal_n) * 100,
                fossorial_freq = fossorial_n / sum(fossorial_n) * 100,
                ground_freq    = ground_n / sum(ground_n) * 100,
                semi_aq_freq   = semi_aq_n / sum(semi_aq_n) * 100,
                stream_freq    = stream_n / sum(stream_n) * 100) %>%
  dplyr::select(c(diff_4C_cat, all_freq:stream_freq)) %>%
   knitr::kable(col.names = c("ΔDuration (months)", "All (%)", "Aquatic (%)", "Arboreal (%)", "Fossorial (%)", "Ground-dwelling (%)", "Semi-aquatic (%)", "Stream-dwelling (%)"),
                digits = 2) # 2 decimal places
```

# Systematic search {-} 

## Exclusion criteria {-}

- Studies with languages that were not translatable on Google Translate.
- Data from experimental treatments such as pathogen infection, pollutants or pesticide contaminants, and pharmacological manipulation (e.g., antidiuretic hormones).
- Data presented as percentage change in body weight without the initial weight.
- Data present with no time component. E.g. Change in body weight until 20% of water loss was reached (% change in mass).
- Studies where units were not presented.

## Preferred Reporting Items for Systematic Reviews and Meta-Analyses {-}

The following Boolean search string for Web of Science: (frog OR toad OR anur\*) AND (water loss OR water uptake OR evaporate\* OR skin resist\* OR cutaneous evapo\*) AND (dehydration OR desiccation OR water OR soil moist\*) NOT (tadpole OR larva\* OR mice OR rat OR plant, OR seed OR fish) and for Scopus: (TITLE-ABS-KEY(frog OR toad OR anur\*)  AND TITLE-ABS-KEY(water AND loss OR water AND uptake OR evaporate\* OR skin AND resist\* OR cutaneous AND evapo\*)) were performed on the 21 July 2022, resulting in 449 records from 1938 to 2022 (WoS), and 51 records from 1951 to 2022 (Scopus). We excluded topics related to chemistry, physical, materials science, medical, geology, fisheries, oceanography, and engineering. Title and abstract screening of the comprehensive search was conducted in [Rayyan](https://www.rayyan.ai/) [@Ouzzani2016]. Forward and backward searches were also conducted on Google Scholar (until 15 Aug 2022; **Fig. S1**) for additional papers, and we also searched for papers from @Feder1992, @Lillywhite2006, and @Hillman2009. Lastly, additional studies (*n* = 3) were provided by the Stellenbosch University librarian (searched 1st Feburary 2023).

![](Fig S3 - PRISMA diagram.png)

**Fig. S3.** PRISMA flow diagram for the systematic data-collection process. *n* = number of papers remaining after each stage of selection. *k* is the number of observations/records, and *n* in the last row is number of species after processing data.

### Definitions {-}

We note, that "water-proof" is a subjective term, and the mechanism is only well understood for the *Phyllomedusa* genus with the presence of waxy secretion of cutaneous lipids, and the stereotypical wiping behaviour [@Blaylock1976; @Gomez2006]. Other arboreal species show dry mucus film on the skin or the presence of cutaneous surafce fluid that may contribute to high skin resistance [@Kobelt1986]. We labelled frogs as "water-proof" based on each study's definition, i.e., if the authors describe the species as "water-proof". However, not all "water-proof" frogs excrete water-proof secretions throughout the year, and may only show this adaptation during certain periods such as the dry season (R. P. Bovo, pers. comm.). 

Another broad post-hoc definition for classifying water-proof is whether the skin resistance is above a certain threshold. @Hillman2009 defined anurans as moderately water-proof with skin resistance between 2 to 20 cm s^-1^, and highly water-proof with skin resistance >20 cm s^-1^. However, even within studies, the same species will show different skin resistance depending on the environmental exposure. e.g. lower skin resistance at higher temperature [@Buttemer2003]. We included both definitions in the raw data file, but used the prior definition for the analysis.

EWL measurements labelled with "cocoon" is straight-forward, with the presence of accumulated epidermal layers in aestivating anurans. EWL measurements labelled with "hollow" were conducted with the presence of a hiding spot protecting the frog from the exposed air.

Anurans also change EWL across the measured period (i.e. decrease in EWL as they become more dehydrated). However, most studies either took an average value over the experiment duration, or presented the first hour of measurements. When studies provided change in EWL at different time points, we took the first hour EWL measurements. Note, almost all animals were considered fully hydrated during the start of the EWL experiment, where they were submersed or given water prior to the EWL experiment. 

## Unpublished data {-}

Additional EWL, skin resistance, and WU data were obtained from unpublished sources. The *Atelopus carrikeri* data is available on an online repository ([Solano & Albert, 2000](https://data.mendeley.com/datasets/4c5nkprzcy/1)), and the *Thoropa miliaris* data can be obtained from C. Navas (navas@usp.br).

Briefly, the method for *Atelopus carrikeri* is described below:

15 *Atelopus carrikeri* were collected near the basin of Pico Cristòbal Colòn (10°54'05.8"N 73°55'00.7"W) and brought back to the Universidad del Magdalena campus. To estimate evaporative water loss, fully hydrated individuals were briefly dried with absorbent paper and the urine was removed by lightly pressing their abdomen and weighed to obtain the initial weight. Each individual was placed in a wind tunnel at 2.4 cm s^-1^, and weighed every five minute until individuals reached 70-65% of their initial weight [@Titon2010]. The rate of water loss was calculated from the value of the regression of the weight lost over time and expressed in mg min^-1^. Evaporative water loss corrected for surface area was calculated by dividing by 2/3 of the total surface area which corresponds to the region of the body in contact with the air flow (mg cm^-2^ min^-1^) [@Withers1982; @Titon2010; @Titon2015]. 

To estimate rate of water uptake, Individuals where placed in containers with absorbent towels moistened with enough water to cover the ventral region after the evaporative water loss experiment. The individuals were dried and weighed every four minutes for six consecutive times [@Titon2010; @Anderson2017]. The water uptake rate per area, was calculated from the value of the regression of the body weight gained as a function of time (mg min^-1^), and in turn divided by 1/3 of the total surface area that corresponds to the ventral area in contact with water during absorption, the values were expressed in units of mg cm^-2^ min^-1^. All experiments were conducted at an average air temperature of 25.55 ± 0.28°C. The relative humidity was not measured but appoximated based on the location and time of the experiment (R. Solano pers. comm.)

The method for *Thoropa miliaris* is described below:

14 *Thoropa miliaris* were collected around Ubatuba (approximate location 23°26′13″S 45°04′08″W), and 15 individuals were collected in the Serra do Mar State Park, Picinguaba Nucleus (approximate location 23°27’50”S to 23°15’00”S and 45°15’00”W to 44°43’30”W). Animals were brought back to the University of São Paulo campus and kept individually in plastic enclosures (17 x 18 x 107
cm) for 5-7 days prior to estimating water loss. Animals were fed captive cockroaches (*Nauphoeta cinérea*) twice a week and were provided fresh water ad libitum (tap water) in a plastic cup. To estimate evaporative water loss, fully hydrated individuals were briefly dried with absorbent paper and the urine was removed by lightly pressing their abdomen and weighed to obtain the initial weight. Each individual was placed in a wind tunnel at 235 cm s^-1^, and weighed every five minute until individuals reached 90% of their initial weight or the animal did not regain posture when turned over. Each individual measurement was replicated twice over the (3 days in between measurements) and averaged. The rate of water loss from the average values was calculated and presented as in mg h^-1^. Evaporative water loss corrected for surface area was calculated by dividing by 2/3 of the total surface area which corresponds to the region of the body in contact with the air flow (mg cm^-2^ min^-1^). The experiment was conducted at an air temperature between 22-25°C at a relative humidity of 50-60%.

Data provided by R. Parelli Bovo (rpbovo@gmail.com) was originally used for a separate paper which is currently in development. However, he has kindly offered to provide the data for this study pre-submission. The species that were collected by R. Parelli Bovo are provided below:

- *Adenomera marmorata*
- *Ameerega flavopicta*
- *Aplastodiscus leucopygius*
- *Boana pardalis*
- *Boana raniceps*
- *Bokermannohyla alvarengai*
- *Bokermannohyla circumdata*
- *Bokermannohyla saxicola*
- *Brachycephalus pitanga*
- *Dendropsophus microps*
- *Dendropsophus sanborni*
- *Dermatonotus muelleri*
- *Elachistocleis cesarii*
- *Haddadus binotatus*
- *Ischnocnema guentheri*
- *Ischnocnema parva*
- *Leptodactylus fuscus*
- *Leptodactylus syphax*
- *Odontophrynus americanus*
- *Phyllomedusa megacephala*
- *Physalaemus atlanticus*
- *Physalaemus olfersii*
- *Pithecopus azureae*
- *Proceratophrys appendiculata*
- *Proceratophrys cururu*
- *Rhinella diptycha*
- *Rhinella ornata*
- *Rhinella rubescens*
- *Scinax fuscovarius*
- *Trachycephalus mesophaeus*

Adults were collected along elevational gradients from sea level to 1,600 m a.s.l in Brazil’s Atlantic Forest and Cerrado during warm/wet seasons (September to February of 2011–2017). Animals were transported to the laboratory (613 m: -22.397331° S, 47.547799° W). All animals were kept individually in plastic terraria, with shelter and tap water ad libitum, under natural thermal/humidity regimes (daily temperature range of 22–27 °C, air humidity of 45–65%) and photoperiod (light/dark cycles 13.5:10.5).

Measurements were taken once for each individual, in the following sequence: evaporative water loss (EWL) and water uptake (WU) within 3-5 days after field collection. Animals were not fed as the measurments were taken in a fasted state. Before and after any experimental run, animals were checked for motor coordination, skin color, posture, and responsiveness. When individuals failed these checks, they were not include in the dataset and subsequent analyses.

EWL and WU experiments were conducted following @Anderson2017, @Gouveia2019, and @Bovo2023. Briefly, animals’ temperature and hydration state were standardized before the trials by holding each amphibian in individual PVC containers, filled with 0.5 cm (body sizes < 5 g) or 1 cm of water (body sizes > 5 g), and placed inside a climate-controlled incubator (122FC model - Eletrolab) at 25 °C for 1 h. To obtain the initial standardised body mass (100% hydrated), frogs were gently dried with absorbent paper and their abdomen were lightly pressed to remove urine from their urinary bladder. Then, frogs were individually placed in PVC containers (1,000 ml) and EWL rates were measured in a typical open-flow system at 25 °C, containing a mass flow meter (SS-3 Subsampler, Sable Systems) supplying a stable airflow at 21.66 cm^3^ s^-1^ (1,300 ml min^-1^), a RH/Dewpoint Controller (DG-4, Sable Systems) standardizing the incurrent relative humidity at 30%, and a water vapor analyzer (RH-300 RH/Dewpoint Analyzer, Sable Systems) quantifying incurrent and excurrent air. All equipment was interfaced to a computer by an analog/digital unit (UI2 – Sable Systems) to record changes in airflow and water vapor density (WVD) every 1.0 second. Skin body temperatures were taken at the end of the experiments, typically 30-40 min for frogs < 5 g and 60 min for frogs > 5 g, for quantification of the WVD at saturation at the evaporating (skin) surface. To quantify the EWL rates, the water vapor density deficit was calculated by the difference between an empty container and one containing the animal (Spotila and Berman 1976). Then, total transepithelial EWL was corrected for unit area of exposed skin surface (2/3 of the total surface; @McClanahan1969) and expressed as mass of water per exposed surface area per time (e.g. μg H~2~O cm^−2^ s^−1^). To minimize the animal’s activity during EWL measurements, experiments were performed during the day (opposite to their natural active period) with chambers in the dark. To detect any significant changes in behavior and posture that could affect the records, individuals were often visually inspected during trials. Data were discarded if an animal urinated during the experiment.

Immediately after the EWL trials, where animals usually lost 98-70 % of their initial body masses (see dataset), each individual was placed in a container (a Petri dish for body sizes smaller than 5 g, or a circular PVC container for body sizes larger than 5 g) filled with tap water at a depth sufficient to cover their ventral region. Animals were taken from the container and carefully blotted with paper tissue and weighed (± 0.0001 g or 0.01 g) every 2 minutes for six consecutive times in a room at 25 °C. WU rates were calculated from the linear regression between body mass increments against time. Then, using the estimated surface area in contact with water (1/3 of the total surface, @McClanahan1969), the WU rate was calculated and expressed per unit area (e.g. μg H~2~O cm^−2^ s^−1^).

Data for *Breviceps montanus* provided by S. Clusella-Trullas (sct333@sun.ac.za) is used for a manuscript that is currently in submission. However, she has kindly offered to provide the data for this study pre-submission.

The raw data, including collection sites, are provided on [GitHub](https://github.com/nicholaswunz/global-frog-drought/tree/main/data).

***

## Calculations and conversions {-}

### Evaporative water loss {-}
The relative skin resistance, $r_i$, is calculated as: 

\begin{equation}
r_i = r_t - r_b,
(\#eq:rest)
\end{equation}

where $r_t$ is the total resistance (s cm^-1^) and $r_b$ is the boundary layer resistance based on an equivalent agar model (s cm^-1^) or hypothetical biophysical model \@ref(eq:rb). $r_t$ is calculated following @Spotila1976 and @Hillman2009:

\begin{equation}
r_t = \frac{\rho}{\textrm{CWL}} = (\rho_{v[skin]} - \rho_v \times \textrm{RH}_{[excurrent]}) \times \textrm{CWL},
(\#eq:trest)
\end{equation}

where $\rho$ is the vapour density gradient at the surface of the animal (g cm^-3^), $\textrm{CWL}$ is the cutaneous rates of evaporative water loss ($\textrm{EWL}$) by surface area (g s^-1^ cm^-2^), $\rho_v$ is the water vapour density of water-saturated air (g cm^-3^), $\rho_{v[skin]}$ is the $\rho_v$ at the skin surface, and $\textrm{RH}$ is the fractional saturation (0-1). $\rho$ is calculated as the difference in the water vapour density at the temperature of the evaporating surface by the water vapour density in the air at $\textrm{RH}$ as fractional saturation [@Feder1992].

$\textrm{EWL}$ (g s^-1^) can be measured gravimetrically (change in mass over time) or changes in water vapour pressure, $e$ (kPa), or $\textrm{RH}$ between the incurrent and excurrent air for a flow-through system. In a flow-through system, $\textrm{EWL}$ can be calculated from @Hillman2009 and @Riddell2017:

\begin{equation}
\textrm{EWL} = \frac{e}{T \times R_v} \times \textrm{FR} = [(\rho_v \times \textrm{RH}_{[incurrent]}) - (\rho_v \times \textrm{RH}_{[excurrent]})] \times \textrm{FR},
(\#eq:ewl)
\end{equation}

where $T$ is temperature in Kelvin (K),  $R_v$ is the gas constant for water vapor (461.5 J K^-1^ kg^-1^), and $\textrm{FR}$ is the flow rate (ml s^-1^)

### Boundary layer resistance {-}
The boundary layer resistance is the layer of air in which an object (or organism) exchanges heat and mass with its surrounding environment. The boundary layer adds to the resistance of water loss by changing the physical conditions of the microclimate around the organism [@Senzano2022]. If $r_b$ was not reported or not estimated by a representative agar model, we used the theoretical $r_b$ with known forced convection ($V$) from @Riddell2017: 

\begin{equation}
r_b = \frac{0.93 \times \rho \times C_{\rho}}{h_c},
(\#eq:rb)
\end{equation}

where, $\rho$  is the density of the air (kg m^-3^), $C_{\rho}$ is the specific heat of air (kg^-1^ K^-1^) calculated as:

\begin{equation}
C_{\rho} = \frac{1004.84 + (1846.40 \times r_w)}{1 + r_w},
(\#eq:cp)
\end{equation}

where $r_w$ is mixing ratio of water vapour [@Tracy2019] estimated using:

\begin{equation}
r_w = \frac{0.62197 \times 1.0053 \times e}{p - 1.0053 \times e},
(\#eq:rw)
\end{equation}

where $e$ is the vapour pressure (kPa) and $p$ is the atmospheric pressure (kPa). $h_c$ \@ref(eq:rb) is the coefficient of convective-heat transfer under forced convection approximated by:

\begin{equation}
h_c = 0.0923(V^{0.0333} \times D^{-0.666}),
(\#eq:hc)
\end{equation}

where $V$ is the wind speed (m s^-1^) and $D$ is the characteristic dimension (m). $D$ is the cross-sectional radius, assuming the dimensions of a sphere for a frog, based on the mass and the total surface area.

If the study was conducted under free convection, we used a modified \@ref(eq:hc) from @Riddell2017:

\begin{equation}
h_c = \frac{0.48 \times \textrm{Gr}^{0.25} \times k}{D},
(\#eq:hcfree)
\end{equation}

where $\textrm{Gr}$ is the Grashof number, $k$ is the thermal conductivity of the fluid (W m^-1^ K^-1^) $D$ is the characteristic dimension (m). $\textrm{Gr}$ describes the relative strength of the buoyant forces to viscous force of the air surrounding the object calculated as:

\begin{equation}
\textrm{Gr} = \frac{g \times \beta \times \Delta{T} \times D^{3}}{v^{2}},
(\#eq:gr)
\end{equation}

where $g$ is the acceleration by gravity (9.80 m s^-2^), $\beta$ is the coefficient of volumetric expansion (3.67 $\times$ 10^-3^ °C^-1^), $D$ is the characteristic dimension, $v$ is the kinematic viscosity from @Riddell2017, and $\Delta{T}$ is estimated by:

\begin{equation}
\Delta{T} = T_0(1+0.38 \times e_0 / p) - T(1+0.38 \times e/p),
(\#eq:delta)
\end{equation}

where $T_0$ is the temperature (K) of the surface, $e_0$ is the water vapour pressure directly above the surface, and $p$ is the atmospheric pressure [@Montieth2013]. 

### Vapour pressure deficit {-}

The vapour pressure deficit was calculated as:

\begin{equation}
VPD = e_s - e_a,
(\#eq:vpd)
\end{equation}

where $e_s$ is the saturation vapour pressure (kPa) at a given temperature and $e_a$ is the actual vapour pressure in the air (kPa). $e_s$ was calculated using the Clausius-Clapeyron equation @Stull2000:

\begin{equation}
e_s = e_0 \times \exp \left[\frac{L}{R_v} \left( \frac{1}{T_0} - \frac{1}{T} \right) \right],
(\#eq:es)
\end{equation}

where $e_0$ = 0.611 kPa and $T_0$ – 273 K are constant parameters, and $R_v$ – 461.5 J K^-1^ Kg^-1^ and $L$ = 2.5 x 106 J kg^-1^ are gas constant for water vapour and the latent heat of vapourization, respectively.  $T$ (as K) is the actual temperature measured. $e_a$ was approximated as:

\begin{equation}
e_a = \frac{\textrm{RH} \times e_s}{100},
(\#eq:ea)
\end{equation}

where $\textrm{RH}$ is the measured relative humidity (%).

### Surface area {-}

For studies that presented EWL and WU without correcting for surface area, we corrected to surface area by dividing the absolute rates by 2/3 of the total surface area for EWL, which corresponds to the area exposed to air when anurans keep the water conservation posture [@Withers1982; @Withers1984]. For WU, we corrected to surface area by dividing the absolute rates by 1/3 of the total surface area because WU primarily occurs in the pelvic 'seat patch' region of the ventral skin [@Baldwin1974; @McClanahan1969; @Willumsen2007]. Total surface area (cm^2^) was estimated via family-specific mass-surface area scaling relationship from @Klein2016:

\begin{equation}
SA = \beta_0 M^{\beta_1},
(\#eq:sa)
\end{equation}

where $\beta_0$ is the intercept, $M$ is the animal body mass (g), and $\beta_1$ is the slope. If the family-specific mass-surface area scaling relationship was not presented, we used the power equation for all Anura (9.8537$M$^0.6745^) as a conservative estimate.

### Percentage mass change to actual mass change {-}

For studies that presented % change in mass ($\Delta M$) with raw data available on initial mass, we converted percentage change in mass (% h^-1^) back to actual mass change (g h^-1^) following: 

\begin{equation}
\textrm{EWL} = M \times \left(\frac{\Delta M}{100}\right),
(\#eq:ewlper)
\end{equation}

where $M$ is the initial body mass (g), and $\Delta M$ is the percentage change in mass (% h^-1^). Note, studies with % change in body mass without the initial body mass presented were not included.

***

# Dataset {-}

```{r clean, message=FALSE}
# Load and clean raw data
raw_dat <- read.csv(file.path(data_path, "raw_data.csv")) %>%
  dplyr::select(study_ID:unit) %>%
  dplyr::mutate(ecotype  = factor(ecotype),
         family   = factor(family),
         origin   = factor(origin),
         strategy = factor(case_when(strategy == "" ~ "none", TRUE ~ as.character(strategy))),
         strategy = fct_relevel(strategy, "none", "water-proof", "cocoon", "hollow"),
         trait    = factor(trait),
         response = factor(response),
         lnMass   = log(mean_mass_g),
         lnFlow   = log(airflow_cm_s + 1),
         es_kPa   = ifelse(trait == "water loss", 0.611 * exp(2500000 / 461.5 * (1 / 273 - 1 / (trt_temp + 273.15))), NA), # saturation vapor pressure (kPa) at a given temperature
         ea_kPa   = ifelse(trait == "water loss", RH_perc * es_kPa / 100, NA), # actual vapor pressure (kPa)
         VPD_kPa  = es_kPa - ea_kPa,
         lnVPD    = log(VPD_kPa)) %>%
  dplyr::filter(species_phylo != "") # remove rows with no species

ewl_dat <- raw_dat %>%
  dplyr::filter(response == "evaporative water loss" & !is.na(unit_corrected_mean)) %>%
  dplyr::mutate(mg_h_mean = unit_corrected_mean * dors_SA_cm2,
                mg_h_sd   = unit_corrected_sd * dors_SA_cm2,    
                lnMean    = log(mg_h_mean),
                v         = mg_h_sd^2 / sample_size, # sampling variance (v)
                sei       = sqrt(v), # standard error (SE)
                inv       = 1 / sei, # precision (inverse of SE) )
                w         = 1 / v, # weight (inverse of variance) 
                h_70      = (mean_mass_g - (mean_mass_g * 0.7)) / (mg_h_mean * 0.001)) 

resist_dat <- raw_dat %>%
  dplyr::filter(unit == "s cm" & !is.na(unit_corrected_mean))

wu_dat <- raw_dat %>%
  dplyr::filter(response == "water uptake" & !is.na(unit_corrected_mean)) %>%
  dplyr::mutate(mg_h_mean = unit_corrected_mean * vent_SA_cm2,
                mg_h_sd   = unit_corrected_sd * vent_SA_cm2,
                lnMean    = log(mg_h_mean),
                v         = mg_h_sd^2 / sample_size, # sampling variance (v)
                sei       = sqrt(v), # standard error (SE)
                inv       = 1 / sei) # precision (inverse of SE)

#ewl_dat %>%
  #group_by(strategy) %>%
  #summarise(mean = mean(h_70, na.rm = TRUE))
```

There are `r length(unique(ewl_dat$species_phylo))` species with data on evaporative water loss (`r length(unique(ewl_dat$study_ID))` studies), `r length(unique(resist_dat$species_phylo))` species with skin resistance data (`r length(unique(resist_dat$study_ID))` studies), and `r length(unique(wu_dat$species_phylo))` species with water uptake data (`r length(unique(wu_dat$study_ID))` studies). We used the evaporative water loss to analyse differences between ecotype as there are more species represented compared to the skin resistance data. There were three species that were excluded from the analysis because they were not listed in the IUCN Red List and were not present in the phylogenetic tree from @Jetz2018: *Brachycephalus pitanga*, *Elachistocleis cesarii*, and *Leptodactylus luctator*.

## Geographical bias in hydroregulation studies {-}

```{r Fig S4, fig.align='center', fig.height=3.5, fig.width=7, echo = FALSE}
wild_dat <- raw_dat %>% dplyr::filter(origin == "wild")
lab_dat  <- raw_dat %>% dplyr::filter(origin == "lab")

world_map <- map_data("world")
raw_dat %>%
  ggplot() +
  geom_map(data = world_map %>% dplyr::filter(region != "Antarctica"), map = world_map, aes(long, lat, map_id = region), fill = "#dedede", colour = NA) +
  geom_point(aes(x = lon, y = lat)) +
  scale_colour_discrete_sequential(palette = "Viridis") +
  scale_y_continuous(expand = c(0, 0)) +
  scale_x_continuous(expand = c(0, 0)) +
  theme_void() + 
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed()
```

**Fig. S4.** Spatial distribution of hydroregulation studies using wild caught anurans (study *n* = `r length(unique(wild_dat$study_ID))`). `r length(unique(lab_dat$study_ID))` studies used captive raised anurans. There are large regions around central Africa and Eurasia with high amphibian diversity, but no hydrological studies conducted (**Fig. 1b**). 

***

## Prepare phylogeny for analysis {-}

The phylogeny was obtained from @Jetz2018 comprising of 7,238 species. The tree was pruned to match the species extracted for the subsequent analysis.

```{r phylo, message=FALSE, results="hide"}
# Load tree
phylo_tree <- ape::read.tree(file.path(data_path, "amph_shl_new_Consensus_7238.tre"))

# Pruning data and phylogeny
tree_tip_label <- phylo_tree$tip.label # extract tree tip names
sp_list        <- raw_dat$species_phylo # extract species name from mean data
pruned_tree    <- ape::drop.tip(phylo_tree, setdiff(phylo_tree$tip.label, sp_list)) # prune phylo_tree to keep species from the raw data
pruned_tree    <- phytools::force.ultrametric(pruned_tree, method = "extend") # ultrametricize the tree
phylo_cor      <- vcv(pruned_tree, cor = T)

setdiff(raw_dat$species_phylo, rownames(phylo_cor))
setdiff(rownames(phylo_cor), raw_dat$species_phylo)

#ape::is.ultrametric(pruned_tree)
```

# Analysis {-}

The original model incorporated origin (lab-raised or wild-caught) and whether the bladder was void of urine prior to the experiment. However, the model's bulk effective samples size (ESS) was too low, indicating posterior means and medians may be unreliable. Since origin and whether the bladder was emptied did not influence EWL, they were excluded in the final model. For transparency, on average, wild-caught anurans had, on average, lower EWL relative to lab-raised anurans (-0.08 [-0.47:0.31]), and anurans with their bladder voided of urine had lower EWL (-0.17 [-0.59: 0.25]). However there is substantial variability between the wild-caught and lab-raised groups, and whether anurans with their bladder voided or not.

```{r ewl, message=FALSE, cache=TRUE, results="hide"}
#options(brms.backend = "cmdstanr") # Error from using Rstans "error in unserialize(socklist[[n]]) : error reading from connection". Used cmdstanr around it.

priors <- c(prior(normal(0, 3), 'b'), 
            prior(normal(0, 3), 'Intercept'),
            prior(student_t(3, 0, 10), 'sd'), 
            prior(student_t(3, 0, 10), 'sigma'))

ewl_model <- brms::brm(lnMean ~ ecotype + strategy + lnMass + lnVPD + lnFlow + (1 | study_ID) + (1 + lnMass | species_iucn) + (1 | gr(species_phylo, cov = phylo)), 
                                     data    = ewl_dat, 
                                     family  = gaussian(), 
                                     data2   = list(phylo = phylo_cor),
                                     prior   = priors,
                                     chains  = 4, 
                                     cores   = 4, 
                                     iter    = 5e3, 
                                     warmup  = 2.5e3, 
                                     control = list(adapt_delta = 0.99, max_treedepth = 15))

wu_model <- brms::brm(lnMean ~ ecotype + lnMass + trt_temp + hydration + origin + (1 | study_ID) + (1 + lnMass | species_iucn) + (1 | gr(species_phylo, cov = phylo)), 
                       data    = wu_dat %>% 
                        dplyr::group_by(ecotype) %>% 
                        dplyr::filter(n() >= 5), 
                       family  = gaussian(), 
                       data2   = list(phylo = phylo_cor),
                       prior   = priors,
                       chains  = 4, 
                       cores   = 4, 
                       iter    = 5e3, 
                       warmup  = 2.5e3, 
                       control = list(adapt_delta = 0.99, max_treedepth = 15))
```

```{r Fig S5, echo = FALSE, fig.width = 7, fig.height = 3, fig.align =' center'}
ewl_model_pp <- brms::pp_check(ewl_model, type = "scatter_avg")
wu_model_pp  <- brms::pp_check(wu_model, type = "scatter_avg")

cowplot::plot_grid(ewl_model_pp, wu_model_pp, ncol = 2, labels = c('a', 'b'))
```

**Fig. S5.** Scatterplots of the observed data (y) vs the average simulated data (y~rep~) from the posterior predictive distribution for the (**a**) evaporative water loss model, and (**b**) the water uptake model. Dashed line represents a slope of 1. 

## Model output {.tabset .tabset-fade .tabset-pills -}

### Table S3 - EWL model {.tabset .tabset-fade .tabset-pills -} 

**Table S3.** Mean parameter estimates, estimate error, and 95% Bayesian credible intervals for the evaporative water loss model, which includes the intercept ($\beta_0$), ecotype, strategy, the natural logarithm of body mass (lnMass), the natural logarithm of vapour pressure deficit (lnVPD), and the natural logarithm of the experimental flow rate (lnFlow). Group-level effects include the standard deviations ($\sigma$) for study-level observations ($\sigma_{study}^2$), phylogenetic relatedness ($\sigma_{phylogeny}^2$), and the correlation among species ($\sigma_{species}^2$) and body mass ($\sigma_{lnMass}^2$). $R_{marginal}^2$ represents the variance explained by fixed effects, while $R_{conditional}^2$ represents the variance explained by both fixed effects and group-level effects.

```{r tableS3, echo = FALSE}
# Fixed effect
fef <- brms::fixef(ewl_model) %>% 
  as.data.frame(.) %>%
  tibble::rownames_to_column("Parameter") %>% 
  dplyr::mutate(Parameter = str_replace(Parameter, "M", "-")) %>% 
  dplyr::mutate(Parameter = case_when(
    Parameter == "Intercept" ~ "ln$\\beta_0$",
    Parameter == "ecotypeArboreal"  ~ "Arboreal",
    Parameter == "ecotypeFossorial"  ~ "Fossorial",
    Parameter == "ecotypeGround-dwelling"  ~ "Ground-dwelling",
    Parameter == "ecotypeSemi-aquatic"  ~ "Semi-aquatic",
    Parameter == "ecotypeStream-dwelling"  ~ "Stream-dwelling",
    Parameter == "strategywater-proof"  ~ "Water-proof",
    Parameter == "strategycocoon"  ~ "Cocoon",
    Parameter == "strategyhollow"  ~ "Hollow",
    Parameter == "ln-ass"  ~ "lnMass",
    Parameter == "lnVPD"  ~ "lnVPD",
    Parameter == "lnFlow"  ~ "lnFlow"),
    ) %>% 
  tibble::add_row(Parameter = "**Fixed effects**", .before = 1)

# Random effect
ref <- summary(ewl_model)$random %>% 
  bind_rows() %>% # unlist
  tibble::rownames_to_column("Parameter") %>% 
  dplyr::select(1:5) %>% 
  dplyr::mutate(Parameter = c("$\\sigma_{species}^2$", 
                              "$\\sigma_{lnMass}^2$", 
                              "cor($\\sigma_{species}^2$, $\\sigma_{lnMass}^2$)", 
                              "$\\sigma_{phylogeny}^2$", 
                              "$\\sigma_{study}^2$")) %>%   
  dplyr::rename(Q2.5  = 4, 
                Q97.5 = 5) %>% 
  tibble::add_row(Parameter = "**Group-level effects**", .before = 1)

ewl_lambda <- hypothesis(ewl_model, "sd_species_phylo__Intercept^2 / (sd_species_phylo__Intercept^2 + sigma^2) = 0", class = NULL)$hypothesis %>% 
  dplyr::select(-c(1,6:8)) %>% 
  dplyr::mutate(Parameter = "$\\lambda$") %>% 
  dplyr::rename(Q2.5  = CI.Lower, 
                Q97.5 = CI.Upper) %>% 
  tibble::add_row(Parameter = "**Phylogenetic signal**", .before = 1)

ewl_r2 <- data.frame(Parameter = "$R_{marginal}^2$",
                     Estimate = round(data.frame(performance::r2_bayes(ewl_model))[2,1], 4) * 100,
                     Q2.5     = round(data.frame(performance::r2_bayes(ewl_model))[2,4], 4) * 100,
                     Q97.5    = round(data.frame(performance::r2_bayes(ewl_model))[2,5], 4) * 100) %>%
  tibble::add_row(Parameter = "$R_{conditional}^2$",
                     Estimate = round(data.frame(performance::r2_bayes(ewl_model))[1,1], 4) * 100,
                     Q2.5     = round(data.frame(performance::r2_bayes(ewl_model))[1,4], 4) * 100,
                     Q97.5    = round(data.frame(performance::r2_bayes(ewl_model))[1,5], 4) * 100) %>% 
  tibble::add_row(Parameter = "**Variance**", .before = 1)

# Render table
bind_rows(fef, ref, ewl_lambda, ewl_r2) %>% 
  remove_rownames() %>% 
  knitr::kable(digits = 2)
```

### Table S4 - WU model {.tabset .tabset-fade .tabset-pills -} 

**Table S4.** Mean parameter estimates, estimate error, and 95% Bayesian credible intervals for the water uptake model, which includes the intercept ($\beta_0$), ecotype, the natural logarithm of body mass (lnMass), treatment temperature, initial hydration level, and origin (lab-raised or wild caught). Group-level effects include the standard deviations ($\sigma$) for study-level observations ($\sigma_{study}^2$), phylogenetic relatedness ($\sigma_{phylogeny}^2$), and the correlation among species ($\sigma_{species}^2$) and body mass ($\sigma_{lnMass}^2$). $R_{marginal}^2$ represents the variance explained by fixed effects, while $R_{conditional}^2$ represents the variance explained by both fixed effects and group-level effects.

```{r tableS5, echo = FALSE}
# Fixed effect
fef <- brms::fixef(wu_model) %>% 
  as.data.frame(.) %>%
  tibble::rownames_to_column("Parameter") %>% 
  dplyr::mutate(Parameter = str_replace(Parameter, "M", "-")) %>% 
  dplyr::mutate(Parameter = case_when(
    Parameter == "Intercept" ~ "ln$\\beta_0$",
    Parameter == "ecotypeFossorial"  ~ "Fossorial",
    Parameter == "ecotypeGround-dwelling"  ~ "Ground-dwelling",
    Parameter == "ecotypeSemi-aquatic"  ~ "Semi-aquatic",
    Parameter == "ln-ass"  ~ "lnMass",
    Parameter == "trt_temp"  ~ "Treatment temperature",
    Parameter == "hydration"  ~ "Initial hydration",
    Parameter == "originwild"  ~ "Origin - Wild",
        TRUE   ~ Parameter)) %>% 
  tibble::add_row(Parameter = "**Fixed effects**", .before = 1)

# Random effect
ref <- summary(wu_model)$random %>% 
  bind_rows() %>% # unlist
  tibble::rownames_to_column("Parameter") %>% 
  dplyr::select(1:5) %>% 
  dplyr::mutate(Parameter = c("$\\sigma_{species}^2$", 
                              "$\\sigma_{lnMass}^2$", 
                              "cor($\\sigma_{species}^2$, $\\sigma_{lnMass}^2$)", 
                              "$\\sigma_{phylogeny}^2$", 
                              "$\\sigma_{study}^2$")) %>%   
  dplyr::rename(Q2.5  = 4, 
                Q97.5 = 5) %>% 
  tibble::add_row(Parameter = "**Group-level effects**", .before = 1)

wu_lambda <- hypothesis(wu_model, "sd_species_phylo__Intercept^2 / (sd_species_phylo__Intercept^2 + sigma^2) = 0", class = NULL)$hypothesis %>% 
  dplyr::select(-c(1,6:8)) %>% 
  dplyr::mutate(Parameter = "$\\lambda$") %>% 
  dplyr::rename(Q2.5  = CI.Lower, 
                Q97.5 = CI.Upper) %>% 
  tibble::add_row(Parameter = "**Phylogenetic signal**", .before = 1)

wu_r2 <- data.frame(Parameter = "$R_{marginal}^2$",
                     Estimate = round(data.frame(performance::r2_bayes(wu_model))[2,1], 4) * 100,
                     Q2.5     = round(data.frame(performance::r2_bayes(wu_model))[2,4], 4) * 100,
                     Q97.5    = round(data.frame(performance::r2_bayes(wu_model))[2,5], 4) * 100) %>%
  tibble::add_row(Parameter = "$R_{conditional}^2$",
                     Estimate = round(data.frame(performance::r2_bayes(wu_model))[1,1], 4) * 100,
                     Q2.5     = round(data.frame(performance::r2_bayes(wu_model))[1,4], 4) * 100,
                     Q97.5    = round(data.frame(performance::r2_bayes(wu_model))[1,5], 4) * 100) %>% 
  tibble::add_row(Parameter = "**Variance**", .before = 1)

# Render table
bind_rows(fef, ref, wu_lambda, wu_r2) %>% 
  remove_rownames() %>% 
  knitr::kable(digits = 2)
```

### Fig. S6 - EWL model {.tabset .tabset-fade .tabset-pills -} 

```{r Fig S6, fig.align='center', fig.height=7, fig.width=7, echo = FALSE}
ewl_ce <- brms::conditional_effects(ewl_model, c("ecotype", "strategy"))
ewl_eco_ce <- data.frame(ewl_ce[[1]]) %>%
  dplyr::rename(estimate = estimate__, ci.lb = lower__, ci.ub = upper__)
ewl_stra_ce <- as.data.frame(ewl_ce[[2]]) %>%
  dplyr::rename(estimate = estimate__, ci.lb = lower__, ci.ub = upper__)

ewl_eco_plot <- ewl_eco_ce %>%
  ggplot(aes(x = ecotype, y = exp(estimate))) + 
  ggforce::geom_sina(data = ewl_dat %>% dplyr::filter(strategy %in% c("none", "water-proof")), aes(x = ecotype, y = mg_h_mean, size = inv), colour = "#dedede") + 
  geom_point(aes(), size = 4, show.legend = FALSE) +
  geom_errorbar(aes(ymin = exp(ci.lb), ymax = exp(ci.ub)), size = 0.8, width = 0.1, show.legend = FALSE) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(0.1, 1, 10, 100, 1000), labels = c(0.1, 1, 10, 100, "1,000")) +
  labs(x = NULL, size = "Precision") + 
  ylab(expression("EWL"~("mg"~H[2]*O~h^{"-1"}))) +
  mytheme() + theme(legend.position = "bottom")

ewl_stra_plot <- ewl_stra_ce %>%
  ggplot(aes(x = strategy, y = exp(estimate))) + 
  ggforce::geom_sina(data = ewl_dat, aes(x = strategy, y = mg_h_mean, size = inv), colour = "#dedede", maxwidth = 0.5) + 
  geom_point(aes(), size = 4, show.legend = FALSE) +
  geom_errorbar(aes(ymin = exp(ci.lb), ymax = exp(ci.ub)), size = 0.8, width = 0.1, show.legend = FALSE) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(0.1, 1, 10, 100, 1000), labels = c(0.1, 1, 10, 100, "1,000")) +
  labs(x = "Strategy", size = "Precision") + 
  ylab(expression("EWL"~("mg"~H[2]*O~h^{"-1"}))) +
  mytheme() + theme(legend.position = "bottom")

cowplot::plot_grid(ewl_eco_plot, ewl_stra_plot, labels = c('a', 'b'), ncol = 1, align = 'v', axis = 'l')
```

**Fig. S6.** Differences in evaporative water loss (mg H~2~O h^-1^) by (**a**) ecotype and (**b**) water-conserving strategies. Note, when plotting by ecotype, the evaporative loss excludes behavioural water-conserving strategies such as during cocoon-forming and inside hollows. Mean estimates ± 95% CI presented in black points and error bars, while raw values were presented as grey points. The size of the grey points indicates study precision (inverse of standard error).

### Fig. S7 - WU model {.tabset .tabset-fade .tabset-pills -} 

```{r Fig S7, fig.align='center', fig.height=3.5, fig.width=7, echo = FALSE}
wu_ce <- brms::conditional_effects(wu_model, c("ecotype", "lnMass", "hydration"))
wu_eco_ce <- data.frame(wu_ce[[1]]) %>%
  dplyr::rename(estimate = estimate__, ci.lb = lower__, ci.ub = upper__)

wu_eco_ce %>%
  ggplot(aes(x = ecotype, y = exp(estimate))) + 
  ggforce::geom_sina(data = wu_dat, aes(x = ecotype, y = mg_h_mean, size = inv), colour = "#dedede") + 
  geom_point(aes(), size = 4, show.legend = FALSE) +
  geom_errorbar(aes(ymin = exp(ci.lb), ymax = exp(ci.ub)), size = 0.8, width = 0.1, show.legend = FALSE) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(10, 100, 1000, 10000, 100000), labels = c(10, 100, "1,000", "10,000", "100,000")) +
  labs(x = NULL, size = "Precision") + 
  ylab(expression("WU"~("mg"~H[2]*O~h^{"-1"}))) +
  mytheme() + theme(legend.position = "bottom")
```

**Fig. S7.** Differences in cutaneous water uptake (mg H~2~O h^-1^) by ecotype. Mean estimates ± 95% CI presented in black points and error bars, while raw values were presented as grey points. The size of the grey points indicates study precision (inverse of standard error).

***

# *NicheMapR* {-}

To estimate the influence of warming and drought on activity of a hypothetical frog, we simulated a water and heat energy exchange model (**Fig. S8a**) and its interaction with a simulated local microclimate using the *NicheMapR* package [@Kearney2017; @Kearney2020].

![](Fig S8 - Energy and water exchange.png)

**Fig. S8.** Summary water and energy exchange model from @Tracy1976 integrated into *NicheMapR* and water conserving strategies. (**a**) Schematic summary of the exchanges of energy and water between a frog and its environment used to develop the transient-state model of water exchange. In respect to water exchange, the net water loss represents water loss from respiratory, cutaneous, ocular, and cloaca evaporation as well as urinary and faecal water [@Pirtle2019]. (**b**) Behavioural, morphological, and physiological strategies employed by frogs on land to reduce water loss [@Hillman2009].

## Simulate water-conserving behaviours {-}

We modified the current `ectoR_devel` function from *NicheMapR* v3.2.1 to account for the influence of hydration on activity (`behav_functions` function) which can be found in the [behav_functions.R](https://github.com/nicholaswunz/global-frog-drought/blob/main/code/behav_functions.R) file on GitHub. 

**Ask Urtzi to elaborate and modify this section** what was changed and what it represents

When the animal is not active (i.e. is below ground), it is simulated to go to an underground retreat. It selects the shallowest depth with temperatures between Tmax and Tmin. If water = T, the frog selects the shallowest node with temperatures between Tmax and Tmin and a water potential >= -72.5, which was reported as a soil water potential from which *Rana pipiens* could absorb water. When the animal is belowground, it re-hydrates at a rate specified in hyd.rate, if the soil water potential is >= -72.5. Important to note that currently frogs are not re-hydrating when active aboveground.

The “skinwet” term ($p_{wet}$) determines the proportion of the total surface area used in the calculation of mass transfer of water from the surface. Here, $p_{wet}$ was calculated from empirically derived skin resistance following @Pirtle2019:

\begin{equation}
p_{wet} = \frac{1}{h_D \times r_i + (\frac{P_r}{S_c})^\frac{2}{3}}, 
(\#eq:pwet)
\end{equation}

where $r_i$ is the skin’s resistance to water vapor transfer (s m^-1^), $P_r$ is the Prandtl number (dimensionless), $S_c$ is the Schmidt number (dimensionless), $h_D$ is the mass transfer coefficient (m s^-1^). To calculate $p_{wet}$, the mass transfer coefficient ($h_D$) must be known. Mass transfer refers to the movement of a substance though a fluid interface, driven by changes in the concentration gradient. The mass transfer coefficient controls the rate of diffusion and is dependent on velocity, temperature, and physical properties of the interphase. Similar to the heat transfer coefficient, the mass transfer coefficient also has two components derived from free and forced convection. We consider forced convection only (as free convention should be negligible within a measurement chamber), and this can be calculated from the heat transfer coefficient:

\begin{equation}
h_D = (\frac{h_C}{C_p \times \rho}) \times (\frac{P_r}{S_c}) ^ \frac{2}{3}, 
(\#eq:hd)
\end{equation}

where $P_r$ is the Prandtl number (dimensionless), $S_c$ is the Schmidt number (dimensionless), $h_C$ is the heat transfer coefficient (J s^-1^ cm^-2^ K^-1^), $\rho$ is the density of dry air (g cm^-3^), and $C_p$ is the specific heat of air (1.01 J g^-1^ K^-1^).

## Simulate drought and warming {-}

Microclimates represent the physical environments experienced by an organism. They are a necessity for mechanistic niche modelling because it is the environment experienced at the scale of the individual that needs to be provided to the equations of energy and mass balance. The microclimate model was implemented as described by @Kearney2014. Specifically, it was driven by historical 0.05° grid (~5 km) daily weather input layers (air temperature, vapor pressure, wind speed, and cloud cover).

We simulated meteorological drought (defined as less than average rainfall) by modifying the `micro_era5` function from *NicheMapR* to incorporate rainfall (`rainfact`) and relative humidity (`rhfact`) deficit factors. A default factor is 1 which is 100% of the original rainfall or relative humidity. A factor of 0.5 means 50% of the original rainfall or relative humidity. The modified function can be found on the accompanying GitHub page.

To simulate frog activity under drought and warming scenarios, we constructed four climate conditions using the `micro_era5` function: 

1. **current normal scenario** representing the mean annual air temperature and rainfall from 1981–2010,
2. **current drought scenario** representing the mean annual air temperature from 1981-2010 and annual rainfall based on 2017-2019 drought in Australia (**Fig. S9**),
3. **warming normal scenario** representing the “business-as-usual” scenario, where the global surface temperature is estimated to increase by 4°C by 2080–2100 with no effect of drought (+4°C only), and 
4. **warming drought scenario** with a +4°C increasing in air temperature and annual rainfall based on 2017-2019 drought in Australia.

First, download the `mcera5` microclimate data to a local directory to run the `micro_era5` function faster.

```{r mcera_setup, eval=FALSE, echo=T}
# get ERA5 data with package mcera5 
# assign your credentials (register here: https://cds.climate.copernicus.eu/user/register)
uid         <- "$$$$$$"
cds_api_key <- "$$$$$$$$-$$$$-$$$$-$$$$-$$$$$$$$$$$$"
ecmwfr::wf_set_key(user = uid, key = cds_api_key, service = "cds")

# bounding coordinates (in WGS84 / EPSG:4326)
c(-41.190, -20.186) 
xmn <- -42
xmx <- -41
ymn <- -21
ymx <- -20

xmn <- 152
xmx <- 154
ymn <- -28
ymx <- -26

# temporal extent
st_time <- lubridate::ymd("2016:01:01") # earliest sampling date
en_time <- lubridate::ymd("2018:12:31") # latest sampling date

# filename and location for downloaded .nc files
file_prefix <- "era5"
op <- "YOUR DIRECTORY"

# build a request (covering multiple years)
req <- mcera5::build_era5_request(xmin = xmn, xmax = xmx,
                                  ymin = ymn, ymax = ymx,
                                  start_time   = st_time,
                                  end_time     = en_time,
                                  outfile_name = file_prefix)

mcera5::request_era5(request = req, uid = uid, out_path = op)
```

Next, run the `micro_era5` function for the four scenarios in 2017 (representing typical rainfall year), and one for 2019 to validate the observed rainfall data.

```{r micro_run, message=FALSE, cache=TRUE, results="hide"}
source("/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/code/micro_era5_drought.R")

longlat <- c(153.09249, -27.6235) # Karawatha, QLD.

# Current normal 2017 scenario
micro_curr_wet <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 1.2, rhfact = 1, # rain and RH parameters
                             warm = 0, # current climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Current dry 2017 scenario
micro_curr_dry <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 0.5, rhfact = 0.1, # rain and RH parameters
                             warm = 0, # current climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Warming normal 2017 scenario
micro_warm_wet <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 1.2, rhfact = 1, # rain and RH parameters
                              warm = 4, # future climate
                              dstart = "01/01/2017", 
                              dfinish = "31/12/2017",
                              spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                              save = 0)

# Warming dry 2017 scenario (simulating 2019 drought)
micro_warm_dry <- micro_era5(loc = longlat, 
                             runshade = 1, minshade = 0, # shade parameters
                             rainfact = 0.5, rhfact = 0.1, # rain and RH parameters
                             warm = 4, # future climate
                             dstart = "01/01/2017", 
                             dfinish = "31/12/2017",
                             spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                             save = 0)

# Simulate rainfall for 2019 drought
act_dry <- micro_era5(loc = longlat, 
                      runshade = 1, minshade = 0, # shade parameters
                      warm = 0, # current climate
                      dstart = "01/01/2019", 
                      dfinish = "31/12/2019",
                      spatial = '/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/Spatial data/karawatha/era5', # change to your location
                      save = 0)
```

To verify our microclimate models, we plotted the simulated the daily rainfall for 2017 (representing typical rainfall) and 2019 (historical drought) with the [observed daily rainfall ](http://www.bom.gov.au/climate/drought/knowledge-centre/previous-droughts.shtml) in Australia for the following location: Karawatha, Southeast Queensland, Australia (153.09249, -27.6235). Karawatha provides a good case study because there are many ecotypes found in this location: ground-dwelling (e.g. *Rhinella marina*), arboreal (e.g. *Litoria caerulea*), fossorial (e.g. *Cyclorana alboguttata*), semi-aquatic (e.g. *Litoria nasuta*), and this area has experienced drought recently (2019). We extracted rainfall data from a weather station next to Karawatha which experienced "very much below average" rainfall from the Australian Bureau of Meteorology.

```{r Fig S9, fig.align='center', fig.height=3.5, fig.width=8}
# Load observed rainfall for 2019
obs_rainfall <- read.csv(file.path(data_path, "obs_rainfall.csv")) %>%
  tibble::rowid_to_column("DOY") %>%
  dplyr::mutate(date = lubridate::make_date(year = 2019, month = month_num, day = day))

# Merge observed and predicted rainfall together

model_rain_wet <- as.data.frame(micro_curr_wet$RAINFALL) %>%
  tibble::rowid_to_column("DOY") %>%
  rename(rainfall_mm = "micro_curr_wet$RAINFALL") %>%
  merge(obs_rainfall, by = "DOY", all.x = TRUE)

model_2019 <- as.data.frame(act_dry$RAINFALL) %>%
  tibble::rowid_to_column("DOY") %>%
  rename(rainfall_mm = "act_dry$RAINFALL") %>%
  merge(obs_rainfall, by = "DOY", all.x = TRUE)

# 2019 drought validate
rain_dry_plot <- model_2019 %>%
  ggplot() +
  geom_line(aes(x = DOY, y = karawatha_19), colour = "grey") +
  geom_line(aes(x = DOY, y = rainfall_mm), colour = "red", alpha = 0.5) +
  labs(x = NULL, y = 'Daily rainfall (mm)') +
  ggtitle("2019 Drought") +
  scale_y_continuous(lim = c(0, 100), expand = c(0, 0)) +
  scale_x_continuous(expand = c(0, 0)) +
  mytheme() + theme(legend.position = "bottom")

# 2017 normal validate
rain_wet_plot <- model_rain_wet %>%
  ggplot() +
  geom_line(aes(x = DOY, y = karawatha_17), colour = "grey") +
  geom_line(aes(x = DOY, y = rainfall_mm), colour = "red", alpha = 0.5) +
  labs(x = NULL, y = 'Daily rainfall (mm)') +
  ggtitle("2017 Normal") +
  scale_y_continuous(lim = c(0, 100), expand = c(0, 0)) +
  scale_x_continuous(expand = c(0, 0)) +
  mytheme() + theme(legend.position = "bottom")

cowplot::plot_grid(rain_wet_plot, rain_dry_plot, ncol = 2)
```

**Fig. S9.** Simulated rainfall from the `micro_era5` function (red) and the observed rainfall for Karawatha, QLD from the Australian Bureau of Meteorology (grey). The total yearly observed rainfall
for 2017 was as **`r sum(model_rain_wet$karawatha_17)` mm**, and the total simulated yearly rainfall from *NicheMapR* was **`r sum(model_rain_wet$rainfall_mm)` mm**. The total yearly observed rainfall for the 2019 drought was **`r sum(model_2019$karawatha_19)` mm**, and the total simulated yearly rainfall from *NicheMapR* was **`r sum(model_2019$rainfall_mm)` mm**. The average yearly rainfall across 1981-2010 is around **1100 mm** per year.

## Simulate activity {-}

We simulated the potential number of hours for activity in a year ($t_{act}$) which represents the suitable thermal and hydric conditions for the animal to move beyond their retreat to either catch prey or finding mates [@Kearney2020]. The thermoregulatory and hydroregulatory sequence in the model assumes that frogs will only be ‘active’ (i.e. moving beyond their retreat, catching prey, finding mates) between the minimum and maximum body temperature thresholds for foraging, `Tmin` and `Tmax` (°C), and the minimum tolerated hydration, `min.hyd` (%). In addition, if `water.act = T`, the frog will only be active if temperatures are within the suitable temp range, and the frog is not expected to go below allowed hydration levels (e.g. if %hydration is not below `min.hyd`).

To estimate the potential $t_{act}$ for one year, we simulated the hypothetical frog to be active during the day and night (24 hours). While most frogs are nocturnal, there are some frog species found moving during the day. The water hydric parameters (e.g. skin resistance, water uptake rate, dehydration tolerance) were based on the average values of the dataset collected from the PRISMA search. The thermal parameters (minimum and maximum foraging and critical temperature) were based on *Rhinalla marina* which has been verified in @Kearney2008.

Three water-saving strategies were constructed to broadly reflect each ecotype that use either behavioural strategies such as microhabitat selection, or physiological strategies such as increased skin resistance or skin thickness (**Table S5**). For example, many amphibians (espically arid specialist) seek or burrow underground to regulate body temperature and water balance without exhibiting thicker skin or higher skin resistance. This is because underground burrows are generally cooler, less varied temperature fluctuation, and more moist relative to the surface climate. 

**Table S5.** Modified parameters for the `sim.ecto` function for each hypothetical frog model. The skin resistance values were based on the average empirical measurements for a typical frog and a waterproof frog in the meta-analysis. Seek shade represents microclimate simulated under shaded conditions (0-90% shade). Retreat underground represents the ability for the frog to seek suitable microclimates underground. Can climb represents the ability for the frog to seek suitable microclimates above ground level (e.g. trees, cliffs). Note, frogs are not active when underground, and water uptake only occurs during inactivity.

```{r table S5, echo=FALSE, message=FALSE}
data.frame(strategy = c("Shade only", "Waterproof","Fossorial"), 
           skin = c("Low", "High", "Low"),
           shade = c("Yes", "Yes", "Yes"),
           underground = c("No", "No", "Yes"),
           climb = c("No", "Yes", "No"),
           ecotype = c("Ground-dwelling", "Arboreal", "Fossorial")) %>%
  knitr::kable(col.names = c("Water-saving strategy", "Skin resistance", "Seek shade?", "Retreat underground?", "Can climb?", "Ecotype")) 
```

It is important to note that we focus only on the direct effects of environmental change on water budgets by predicting changes in EWL rates. The conclusions we draw do not account for the indirect effects of environmental change on water budgets, such as potential changes to thermal tolerance as a result of dehydration, or the thermoregulatory difficulties that may arise as a result of habitat modification.

```{r frog_sim, message=FALSE, cache=TRUE, results="hide"}
source("/Users/nicholaswu/Library/CloudStorage/OneDrive-WesternSydneyUniversity/Drought project/code/behav_functions.R")
# Construct frog model
# compute the heat exchange by convection (extract mass transfer coefficient, Prandtl number and Schmidt number)
CONV_out <- NicheMapR::CONV_ENDO(TS     = 19, # skin temperature (°C)
                                 TENV   = 20, # fluid temperature (°C)
                                 SHAPE  = 4, # 4 is ellipsoid
                                 SURFAR = mean(resist_dat$dors_SA_cm2, na.rm = TRUE) / 10000,  # surface area for convection, m2
                                 FLTYPE = 0, # fluid type: 0 = air
                                 FURTST = 0, # test of presence of fur (length x diameter x density x depth) (-)
                                 D      = mean(resist_dat$D, na.rm = TRUE), 
                                 TFA    = 20, # initial fur/air interface temperature
                                 VEL    = mean(resist_dat$airflow_cm_s, na.rm = TRUE) / 100, # wind speed (m/s)
                                 ZFUR   = 0, # fur depth, mean (m)
                                 BP     = 101325, # barometric pressure at sea level
                                 ELEV   = 0) # elevation (m)

# basic parameters for 30 g frog
Ww_g         <- exp(mean(log(raw_dat$mean_mass_g), na.rm = TRUE)) # geometric mean wet weight of animal (g), account for uneven distribution
r_s_low      <- resist_dat %>% dplyr::filter(strategy == "none") %>% dplyr::select(unit_corrected_mean) # skin resistance
pct_wet_high <- 1 / (CONV_out[5] * mean(r_s_low$unit_corrected_mean) + (CONV_out[11] / CONV_out[13]) ^ 0.6666666) * 100  # % of surface area acting as a free-water exchanger (Pirtle et al 2017)

# parameters for a water-proof frog
r_s_high    <- resist_dat %>% dplyr::filter(strategy == "water-proof") %>% dplyr::select(unit_corrected_mean) # skin resistance
pct_wet_low <- 1 / (CONV_out[5] * max(r_s_high$unit_corrected_mean) + (CONV_out[11] / CONV_out[13]) ^ 0.6666666) * 100 # % of surface area acting as a free-water exchanger (Pirtle et al 2017)

# Thermal traits based on Rhinella marina
Tmin   <- 13.7 # minimum Tb at which activity occurs (Kearney et al 2008)
Tmax   <- 30.4 # maximum Tb at which activity occurs (Kearney et al 2008)
#T_pref <- 24 # preferred Tb (Kearney et al 2008)
CTmax  <- 37 # critical thermal minimum (affects choice of retreat) Tracy et al 2012
CTmin  <- 8 # critical thermal maximum (affects choice of retreat) Kolbe et al 2010, McCann et al 2014

# Water balance traits
min_hyd <- 80 # minimum tolerated hydration before activity declines (% of fully hydrated animals)
hyd.death <- 50 # minimum tolerated hydration before death (% of fully hydrated animals)
wu_rate <- wu_dat %>% dplyr::filter(strategy == "none") %>% dplyr::select(mg_h_mean)
hyd_rate <- exp(mean(log(wu_rate$mg_h_mean), na.rm = TRUE)) / 1000 # geometric mean rehydration rate (g/h), account for uneven distribution
# depends on current and max hydration like this: hyd.rate * ((hyd - hyd.current) / hyd)

# behav = 'diurnal', 'nocturnal' or 'both'
# water; does the frog select depth according to water potential? (TRUE or FALSE)
# water.act; does the activity depend on water loss? (TRUE or FALSE)

# SHADE MODEL
shad_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

shad_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = FALSE, water.act = TRUE)

# WATER-PROOF MODEL
tree_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

tree_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = FALSE, climb = TRUE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_low, 
                              water = TRUE, water.act = TRUE)

# BURROWING MODEL
burr_curr_wet_mod <- sim.ecto(micro_curr_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_curr_dry_mod <- sim.ecto(micro_curr_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_warm_wet_mod <- sim.ecto(micro_warm_wet, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)

burr_warm_dry_mod <- sim.ecto(micro_warm_dry, Ww_g = Ww_g, shape = 4, 
                              Tmax = Tmax, Tmin = Tmin, CTmin = CTmin, CTmax = CTmax,
                              behav = 'both', in.shade = TRUE, burrow = TRUE, climb = FALSE,
                              min.hyd = min_hyd, hyd.death = hyd.death,
                              hyd.rate = hyd_rate, pct_wet = pct_wet_high, 
                              water = TRUE, water.act = TRUE)
```


``` {r active}
# SHADE MODEL
shad_curr_wet_df <- data.frame(shad_curr_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = shad_curr_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_wet = length(active[active == TRUE]))

shad_curr_dry_df <- data.frame(shad_curr_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = shad_curr_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_dry = length(active[active == TRUE]))

shad_warm_wet_df <- data.frame(shad_warm_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = shad_warm_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_wet = length(active[active == TRUE]))

shad_warm_dry_df <- data.frame(shad_warm_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = shad_warm_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_dry = length(active[active == TRUE]))

shad_model <- shad_curr_wet_df %>%
  merge(shad_curr_dry_df, by = "day") %>%
  merge(shad_warm_wet_df, by = "day") %>%
  merge(shad_warm_dry_df, by = "day") %>%
  pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
  dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet", "curr_dry", "curr_wet")),
                season = case_when(
    day >= 335 ~ "summer",
    day >= 1 & day <= 61 ~ "summer",
    day >= 62 & day <= 153 ~ "autumn",
    day >= 154 & day <= 244 ~ "winter",
    day >= 245 & day <= 334 ~ "spring"
    ))

# TREE MODEL
tree_curr_wet_df <- data.frame(tree_curr_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = tree_curr_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_wet = length(active[active == TRUE]))

tree_curr_dry_df <- data.frame(tree_curr_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = tree_curr_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_dry = length(active[active == TRUE]))

tree_warm_wet_df <- data.frame(tree_warm_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = tree_warm_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_wet = length(active[active == TRUE]))

tree_warm_dry_df <- data.frame(tree_warm_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = tree_warm_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_dry = length(active[active == TRUE]))

tree_model <- tree_curr_wet_df %>%
  merge(tree_curr_dry_df, by = "day") %>%
  merge(tree_warm_wet_df, by = "day") %>%
  merge(tree_warm_dry_df, by = "day") %>%
  pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
  dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet", "curr_dry", "curr_wet")),
                season = case_when(
    day >= 335 ~ "summer",
    day >= 1 & day <= 61 ~ "summer",
    day >= 62 & day <= 153 ~ "autumn",
    day >= 154 & day <= 244 ~ "winter",
    day >= 245 & day <= 334 ~ "spring"
    ))

# FOSSORIAL MODEL
burr_curr_wet_df <- data.frame(burr_curr_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = burr_curr_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_wet = length(active[active == TRUE]))

burr_curr_dry_df <- data.frame(burr_curr_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = burr_curr_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(curr_dry = length(active[active == TRUE]))

burr_warm_wet_df <- data.frame(burr_warm_wet_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = burr_warm_wet_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_wet = length(active[active == TRUE]))

burr_warm_dry_df <- data.frame(burr_warm_dry_mod$act) %>%
  tibble::rowid_to_column("hour") %>%
  dplyr::rename(active = burr_warm_dry_mod.act) %>%
  dplyr::mutate(day = ceiling(1:8760/24)) %>%
  dplyr::group_by(day) %>%
  dplyr::summarise(warm_dry = length(active[active == TRUE]))

burr_model <- burr_curr_wet_df %>%
  merge(burr_curr_dry_df, by = "day") %>%
  merge(burr_warm_wet_df, by = "day") %>%
  merge(burr_warm_dry_df, by = "day") %>%
  pivot_longer(!day, names_to = "condition", values_to = "hours") %>%
  dplyr::mutate(condition = factor(condition, levels = c("warm_dry", "warm_wet", "curr_dry", "curr_wet")),
                season = case_when(
    day >= 335 ~ "summer",
    day >= 1 & day <= 61 ~ "summer",
    day >= 62 & day <= 153 ~ "autumn",
    day >= 154 & day <= 244 ~ "winter",
    day >= 245 & day <= 334 ~ "spring"
    ))
```

## Model output {.tabset .tabset-fade .tabset-pills -}

### Table S6a - Summary {.tabset .tabset-fade .tabset-pills -} 

**Table S6a.** Total potential activity hours per year ($t_{act}$) for a `r Ww_g` g frog under different warming (current or warming) and drought (normal or drought) conditions with different water-saving strategies.

``` {r table s6a}
data.frame(strategy = c("Shade only", "Shade only", "Shade only", "Shade only",
                        "Waterproof", "Waterproof", "Waterproof", "Waterproof",
                        "Burrowing", "Burrowing", "Burrowing", "Burrowing"),
           temp = c("current", "current", "warming", "warming"),
           rain = c("normal", "drought"),
           t_act_h = c(sum(shad_curr_wet_df$curr_wet), sum(shad_curr_dry_df$curr_dry), sum(shad_warm_wet_df$warm_wet), sum(shad_warm_dry_df$warm_dry),
                       sum(tree_curr_wet_df$curr_wet), sum(tree_curr_dry_df$curr_dry), sum(tree_warm_wet_df$warm_wet), sum(tree_warm_dry_df$warm_dry),
                       sum(burr_curr_wet_df$curr_wet), sum(burr_curr_dry_df$curr_dry), sum(burr_warm_wet_df$warm_wet), sum(burr_warm_dry_df$warm_dry)),
           t_act_per = c(sum(shad_curr_wet_df$curr_wet) / 8760 * 100, sum(shad_curr_dry_df$curr_dry) / 8760 * 100, sum(shad_warm_wet_df$warm_wet) / 8760 * 100, sum(shad_warm_dry_df$warm_dry) / 8760 * 100,
                       sum(tree_curr_wet_df$curr_wet) / 8760 * 100, sum(tree_curr_dry_df$curr_dry) / 8760 * 100, sum(tree_warm_wet_df$warm_wet) / 8760 * 100, sum(tree_warm_dry_df$warm_dry) / 8760 * 100,
                       sum(burr_curr_wet_df$curr_wet) / 8760 * 100, sum(burr_curr_dry_df$curr_dry) / 8760 * 100, sum(burr_warm_wet_df$warm_wet) / 8760 * 100, sum(burr_warm_dry_df$warm_dry) / 8760 * 100)) %>%
  knitr::kable(col.names = c("Water-saving strategy", "Warming simulation", "Drought simulation", "$t_{act}$ (h)", "$t_{act}$ (%)"))
```

### Table S6b - Relative change year {.tabset .tabset-fade .tabset-pills -} 

**Table S6b.** Change in $t_{act}$ (%) for each water-saving strategy relative to the current normal scenario under warming only, drought only, and warming and drought combined. 

``` {r table s6b}
data.frame(strategy = c("Shade only","Waterproof", "Burrowing"),
           curr_wet = c(sum(shad_curr_wet_df$curr_wet), sum(tree_curr_wet_df$curr_wet), sum(burr_curr_wet_df$curr_wet)),
           curr_dry = c(sum(shad_curr_dry_df$curr_dry), sum(tree_curr_dry_df$curr_dry), sum(burr_curr_dry_df$curr_dry)),
           warm_wet = c(sum(shad_warm_wet_df$warm_wet), sum(tree_warm_wet_df$warm_wet), sum(burr_warm_wet_df$warm_wet)),
           warm_dry = c(sum(shad_warm_dry_df$warm_dry), sum(tree_warm_dry_df$warm_dry), sum(burr_warm_dry_df$warm_dry))) %>%
  dplyr::mutate(delta_warm     = (warm_wet - curr_wet) / curr_wet * 100,
                delta_dry      = (curr_dry - curr_wet) / curr_wet * 100,
                delta_warm_dry = (warm_dry - curr_wet) / curr_wet * 100) %>%
  dplyr::select(-c(curr_wet:warm_dry)) %>%
  knitr::kable(col.names = c("Water-saving strategy", "$\\Delta$ warming only (%)", "$\\Delta$ drought only (%)", "$\\Delta$ warming and drought (%)"))
```

### Table S6c - Relative change season {.tabset .tabset-fade .tabset-pills -} 

**Table S6c.** Change in $t_{act}$ (%) for each water-saving strategy relative to the current normal scenario under warming only, drought only, and warming and drought combined. 

``` {r table s6c}
# summer
shad_model_summer <- shad_model %>%
  dplyr::filter(season == "summer") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tree_model_summer <- tree_model %>%
  dplyr::filter(season == "summer") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

burr_model_summer <- burr_model %>%
  dplyr::filter(season == "summer") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tab_summer <- data.frame(strategy = c("Shade only","Waterproof", "Burrowing"),
           curr_wet = c(shad_model_summer$hours[4], tree_model_summer$hours[4], burr_model_summer$hours[4]),
           curr_dry = c(shad_model_summer$hours[3], tree_model_summer$hours[3], burr_model_summer$hours[3]),
           warm_wet = c(shad_model_summer$hours[2], tree_model_summer$hours[2], burr_model_summer$hours[2]),
           warm_dry = c(shad_model_summer$hours[1], tree_model_summer$hours[1], burr_model_summer$hours[1])) %>%
  dplyr::mutate(delta_warm     = (warm_wet - curr_wet) / curr_wet * 100,
                delta_dry      = (curr_dry - curr_wet) / curr_wet * 100,
                delta_warm_dry = (warm_dry - curr_wet) / curr_wet * 100) %>%
  dplyr::select(-c(curr_wet:warm_dry)) %>%
  tibble::add_row(strategy = "**Summer**", .before = 1)

# autumn
shad_model_autumn <- shad_model %>%
  dplyr::filter(season == "autumn") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tree_model_autumn <- tree_model %>%
  dplyr::filter(season == "autumn") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

burr_model_autumn <- burr_model %>%
  dplyr::filter(season == "autumn") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tab_autumn <- data.frame(strategy = c("Shade only","Waterproof", "Burrowing"),
           curr_wet = c(shad_model_autumn$hours[4], tree_model_autumn$hours[4], burr_model_autumn$hours[4]),
           curr_dry = c(shad_model_autumn$hours[3], tree_model_autumn$hours[3], burr_model_autumn$hours[3]),
           warm_wet = c(shad_model_autumn$hours[2], tree_model_autumn$hours[2], burr_model_autumn$hours[2]),
           warm_dry = c(shad_model_autumn$hours[1], tree_model_autumn$hours[1], burr_model_autumn$hours[1])) %>%
  dplyr::mutate(delta_warm     = (warm_wet - curr_wet) / curr_wet * 100,
                delta_dry      = (curr_dry - curr_wet) / curr_wet * 100,
                delta_warm_dry = (warm_dry - curr_wet) / curr_wet * 100) %>%
  dplyr::select(-c(curr_wet:warm_dry)) %>%
  tibble::add_row(strategy = "**Autumn**", .before = 1)

# winter
shad_model_winter <- shad_model %>%
  dplyr::filter(season == "winter") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tree_model_winter <- tree_model %>%
  dplyr::filter(season == "winter") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

burr_model_winter <- burr_model %>%
  dplyr::filter(season == "winter") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tab_winter <- data.frame(strategy = c("Shade only","Waterproof", "Burrowing"),
           curr_wet = c(shad_model_winter$hours[4], tree_model_winter$hours[4], burr_model_winter$hours[4]),
           curr_dry = c(shad_model_winter$hours[3], tree_model_winter$hours[3], burr_model_winter$hours[3]),
           warm_wet = c(shad_model_winter$hours[2], tree_model_winter$hours[2], burr_model_winter$hours[2]),
           warm_dry = c(shad_model_winter$hours[1], tree_model_winter$hours[1], burr_model_winter$hours[1])) %>%
  dplyr::mutate(delta_warm     = (warm_wet - curr_wet) / curr_wet * 100,
                delta_dry      = (curr_dry - curr_wet) / curr_wet * 100,
                delta_warm_dry = (warm_dry - curr_wet) / curr_wet * 100) %>%
  dplyr::select(-c(curr_wet:warm_dry)) %>%
  tibble::add_row(strategy = "**Winter**", .before = 1)

# spring
shad_model_spring <- shad_model %>%
  dplyr::filter(season == "spring") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tree_model_spring <- tree_model %>%
  dplyr::filter(season == "spring") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

burr_model_spring <- burr_model %>%
  dplyr::filter(season == "spring") %>%
  dplyr::group_by(condition) %>%
  dplyr::summarise(hours = sum(hours))

tab_spring <- data.frame(strategy = c("Shade only","Waterproof", "Burrowing"),
           curr_wet = c(shad_model_spring$hours[4], tree_model_spring$hours[4], burr_model_spring$hours[4]),
           curr_dry = c(shad_model_spring$hours[3], tree_model_spring$hours[3], burr_model_spring$hours[3]),
           warm_wet = c(shad_model_spring$hours[2], tree_model_spring$hours[2], burr_model_spring$hours[2]),
           warm_dry = c(shad_model_spring$hours[1], tree_model_spring$hours[1], burr_model_spring$hours[1])) %>%
  dplyr::mutate(delta_warm     = (warm_wet - curr_wet) / curr_wet * 100,
                delta_dry      = (curr_dry - curr_wet) / curr_wet * 100,
                delta_warm_dry = (warm_dry - curr_wet) / curr_wet * 100) %>%
  dplyr::select(-c(curr_wet:warm_dry)) %>%
  tibble::add_row(strategy = "**Spring**", .before = 1)

# Render table
bind_rows(tab_summer, tab_autumn, tab_winter, tab_spring) %>% 
  remove_rownames() %>% 
  knitr::kable(col.names = c("Water-saving strategy", "$\\Delta$ warming only (%)", "$\\Delta$ drought only (%)", "$\\Delta$ warming and drought (%)"))
```

***

# Additional figures {-}

```{r fig S11, fig.align='center', fig.height=3, fig.width=6}
temp_dat <- ewl_dat %>% dplyr::filter(!is.na(trt_temp))

temp_dat %>%
  ggplot(aes(x = trt_temp, y = skin_temp)) +
  geom_point(size = 2, colour = "grey") +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", size = 0.5) +
  stat_smooth(method = "lm", formula = y ~ x + I(x^4), se = F, colour = "black") +
  ylim(5,50) + xlim(5,50) +
  labs(x = "Air temperature (°C)", y = "Skin surface temperature (°C)") +
  mytheme()
```

**Fig. S11.** Relationship between exposed air temperature (°C) during the experiment and the observed skin surface temperature (°C) across `r length(unique(temp_dat$species_phylo))` species. The black line represents the non-linear relationship between air temperature and skin temperature where the skin temperature remains at ~35°C when exposed to air temperatures > 40°C.


# Figures {-}

Code to produce the main document figures are detailed below. Figures produced were further modified in [Adobe Illustrator](https://www.adobe.com/au/products/illustrator.html) for publication.

## Figure 1 - AI risk {-}

```{r Fig 1, fig.align='center', fig.height=7, fig.width=9, fig.show='hide'}
# Reproject maps
rob_proj   <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs" 
world_rob  <- spTransform(world_spdf, CRSobj = rob_proj)

# land
data("land", package = "tmap")
land_raster <- as(land, "Raster") # convert from star to raster
land_df <- raster::as.data.frame(raster::rasterToPoints(land_raster))

elevation_raster <- projectRaster(land_raster$elevation, crs = rob_proj)
slope_raster     <- raster::terrain(elevation_raster, opt = 'slope')
aspect_raster    <- raster::terrain(elevation_raster, opt = 'aspect')
hill_raster      <- hillShade(slope_raster, aspect_raster, 40, 270) #setting the elevation angle (of the sun) to 40 and the direction angle of the light to 270
hill_m           <- rasterToPoints(hill_raster)
hill_df          <-  data.frame(hill_m)
colnames(hill_df) <- c("lon", "lat", "hill")

# Fig 1a - AI current
AI_rob    <- raster::projectRaster(ai_rast, crs = rob_proj)
AI_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_rob)) 
AI_df_rob <- AI_df_rob %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',                       
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid', 
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid',      
    layer >= 0.05 & layer < 0.2 ~ 'Arid',         
    layer < 0.05 ~ 'Hyper-arid'                    
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                                  levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                             'Dry sub-humid', 'Humid'),
                                  ordered = TRUE))

AI_2C_rob    <- projectRaster(ai_2C_rast, crs = rob_proj)
AI_2C_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_2C_rob)) 
AI_2C_df_rob <- AI_2C_df_rob %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',                       
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid', 
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid',     
    layer >= 0.05 & layer < 0.2 ~ 'Arid',         
    layer < 0.05 ~ 'Hyper-arid'                
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                                  levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                             'Dry sub-humid', 'Humid'),
                                  ordered = TRUE))

AI_4C_rob    <- projectRaster(ai_4C_rast, crs = rob_proj)
AI_4C_df_rob <- raster::as.data.frame(raster::rasterToPoints(AI_4C_rob)) 
AI_4C_df_rob <- AI_4C_df_rob %>% 
  # Recode
  dplyr::mutate(category = case_when(
    is.infinite(layer) ~ 'Humid',
    layer >= 0.65 ~ 'Humid',                       
    layer >= 0.5 & layer < 0.65 ~ 'Dry sub-humid', 
    layer >= 0.2 & layer < 0.5 ~ 'Semi-arid',      
    layer >= 0.05 & layer < 0.2 ~ 'Arid',         
    layer < 0.05 ~ 'Hyper-arid'                    
  )) %>% 
  # Convert to ordered factor
  dplyr::mutate(category = factor(category,
                                  levels = c('Hyper-arid', 'Arid', 'Semi-arid',
                                             'Dry sub-humid', 'Humid'),
                                  ordered = TRUE))

arid_col <- c('#8E063B', '#CB6D53', '#E99A2C', '#F5D579', 'white')
aridity_plot <- ggplot() +
  geom_raster(data = AI_df_rob, aes(y = y, x = x, fill = category)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = arid_col,
                    guide = guide_legend(reverse = TRUE)) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Aridity Index (1981-2010)") +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1)  

# Fig 1b - Species richness
anuran_rob    <- projectRaster(anuran_sr, crs = rob_proj)
anuran_df_rob <- raster::as.data.frame(raster::rasterToPoints(anuran_rob)) %>% dplyr::rename(species_n = layer)

sp_breaks = c(1, 5, 25, 100)
anuran_plot <- ggplot() +
  geom_raster(data = anuran_df_rob, aes(y = y, x = x, fill = species_n)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  colorspace::scale_fill_continuous_sequential(palette = "BluGrn", trans = "log",
                                               breaks = sp_breaks, labels = sp_breaks) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle("Anuran species richness") +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed()

AI_merged <- merge(AI_df_rob, AI_2C_df_rob, by = c("x", "y"), all.x = T) %>%
  merge(AI_4C_df_rob, by = c("x", "y"), all.x = T) %>%
  dplyr::rename(layer_current = layer.x,
                category_current = category.x,
                layer_2C  = layer.y,
                category_2C = category.y,
                layer_4C  = layer,
                category_4C = category) %>%
  dplyr::mutate(change_2C = (layer_2C - layer_current / layer_current) * 100,
                change_2C_AI = case_when(
                  change_2C >= -2 & change_2C <2 ~ 'No change to wetter',
                  change_2C >= -10 & change_2C < -2 ~ '>-2 to -10%',
                  change_2C >= -20 & change_2C < -10 ~ '>-10 to -20%',
                  change_2C >= -40 & change_2C < -20 ~ '>-20 to -40%',
                  change_2C >= -80 & change_2C < -40 ~ '>-40 to -80%',
                  change_2C < -80 ~ '<-80%'),
                  change_4C = (layer_4C - layer_current / layer_current) * 100,
                change_4C_AI = case_when(
                  change_4C >= -2 & change_4C <2 ~ 'No change to wetter',
                  change_4C >= -10 & change_4C < -2 ~ '>-2 to -10%',
                  change_4C >= -20 & change_4C < -10 ~ '>-10 to -20%',
                  change_4C >= -40 & change_4C < -20 ~ '>-20 to -40%',
                  change_4C >= -80 & change_4C < -40 ~ '>-40 to -80%',
                  change_4C < -80 ~ '<-80%'
                )) %>% 
  filter(category_current != "Hyper-arid") %>%
  dplyr::mutate(change_2C_AI = factor(change_2C_AI,
                                   levels = c('<-80%', '>-40 to -80%','>-20 to -40%',
                                              '>-10 to -20%', '>-2 to -10%', 'No change to wetter'),
                                   ordered = TRUE),
                change_4C_AI = factor(change_4C_AI,
                                   levels = c('<-80%', '>-40 to -80%','>-20 to -40%',
                                              '>-10 to -20%', '>-2 to -10%', 'No change to wetter'),
                                   ordered = TRUE))

change_col <- c('#B13F63', '#D35D60', '#E38566', '#EEAC7D', '#F5D1A8', 'white')

# Fig 1C - 2C Difference
change_2C_plot <- ggplot() +
  geom_raster(data = AI_merged, aes(y = y, x = x, fill = change_2C_AI)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = change_col, na.value = "white") +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void()+ ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in dryness ("*Delta*"AI +2°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) 

# Fig 1d - 4C Difference
change_4C_plot <- ggplot() +
  geom_raster(data = AI_merged, aes(y = y, x = x, fill = change_4C_AI)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = change_col, na.value = "white") +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void()+ ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in dryness ("*Delta*"AI +4°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) 

# Fig 1e - Dryness and species richness
arid_col_2 <- c("#8E063B", "#CB6D53", "#E99A2C", "#F5D579", "light grey")
species_ai_plot <- ai_sp_df %>% 
  ggplot(aes(x = aridity, y = species_n, colour = category)) +
  geom_point(size = 2) +
  coord_trans(x = "sqrt") +
  scale_colour_manual(values = arid_col_2,
                    guide = guide_legend(reverse = TRUE)) +
  scale_x_continuous(limits = c(0,3),
                     breaks = c(0, 0.05, 0.2, 0.5, 0.65)) +
  xlab("Precipitation / Evapotranspiration") +
  ylab("Species richness") +
  mytheme() + theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks.x = element_blank())

# Fig 1f - Dryness and species richness by ecotype
ecotype_ai_plot <- ai_sp_df %>%
  dplyr::filter(aridity <=3) %>%
  dplyr::select(aridity, species_n:stream_n) %>%
  tidyr::pivot_longer(!aridity, names_to = "ecotype", values_to = "mean") %>%
  dplyr::filter(ecotype != "species_n") %>%
  drop_na(mean) %>%
  dplyr::mutate(ecotype = factor(ecotype, 
                                 levels = c("fossorial_n", "ground_n", "aquatic_n", 
                                            "arboreal_n", "semi_aq_n", "stream_n"))) %>%
  ggplot(aes(x = aridity, y = ecotype, group = ecotype, fill = ecotype)) +
  ggridges::stat_density_ridges(scale = 1, rel_min_height = 0.01, alpha = 0.5,
                                show.legend = FALSE) +
  scale_fill_viridis_d() +
  scale_x_continuous(trans = "sqrt") +
  labs(x = NULL, y = NULL) +
  mytheme()

# Fig 1g - Change in species AI
# Merge summary data by row
arid_sp_comb          <- data.frame(bind_rows(ai_sp_sum, ai_2C_sp_sum, ai_4C_sp_sum))
arid_sp_comb$scenario <- forcats::fct_relevel(arid_sp_comb$scenario, "Current")

species_occ_plot <- arid_sp_comb %>% 
  ggplot(aes(x = category, y = log(all_freq), group = scenario, colour = scenario, shape = scenario)) +
  geom_point(position = position_dodge(width = .5), size = 1.5) +
  scale_colour_manual(values = c("#F5D579", "#CB6D53", "#8E063B")) +
  scale_y_continuous(breaks = pretty(log(arid_sp_comb$all_freq)), labels = pretty(arid_sp_comb$all_freq)) +
  ylab("% species") + xlab(NULL) +
  mytheme() + theme(legend.position = "none")

# Fig 1
plot_grid(species_ai_plot, ecotype_ai_plot, species_occ_plot, align = "v", ncol = 3) # plot in between.

cowplot::plot_grid(aridity_plot + theme(legend.position = "bottom"), 
                   anuran_plot + theme(legend.position = "bottom"), 
                   change_2C_plot + theme(legend.position = "bottom"),
                   change_4C_plot + theme(legend.position = "bottom"),
                   align = "h", axis = "bt",
                   ncol = 2)
```

## Figure 2 - PDSI risk {-}

```{r Fig 2, fig.align='center', fig.height=6, fig.width=9, fig.show='hide'}
PDSI_risk_rob <- raster::projectRaster(raster::stack(PDSI_2C_diff_rast, PDSI_4C_diff_rast, PDSI_freq_diff, PDSI_dur_diff,
                                                     resample(anuran_sr, PDSI_4C_diff_rast)), 
                                       crs = rob_proj)
                             
PDSI_risk_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_risk_rob)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(delta_int_2C_bin = ifelse(delta_int_2C < -1, 1, 0), # delta_PDSI < -1
         delta_int_4C_bin = ifelse(delta_int_4C < -1, 1, 0), # delta_PDSI < -1
         delta_freq_2C_bin = ifelse(delta_freq_2C >1, 1, 0), # month > 1
         delta_freq_4C_bin = ifelse(delta_freq_4C >1, 1, 0), # month > 1
         delta_dur_2C_bin = ifelse(delta_dur_2C >1, 1, 0), # month > 1
         delta_dur_4C_bin = ifelse(delta_dur_4C >1, 1, 0)) %>% # month > 1 
  dplyr::rowwise() %>%
  dplyr::mutate(count_2C = sum(delta_int_2C_bin, delta_freq_2C_bin, delta_dur_2C_bin, na.rm = T),
         
         count_4C = sum(delta_int_4C_bin, delta_freq_4C_bin, delta_dur_4C_bin, na.rm = T),
         risk_2C  = sp_n * count_2C,
         risk_4C  = sp_n * count_4C,
         count_2C = factor(count_2C, levels = c("3", "2", "1")),
         count_4C = factor(count_4C, levels = c("3", "2", "1"))
         ) %>%
  data.frame()

# Plot
PDSI_risk_2C_plot <- ggplot() +
  geom_raster(data = PDSI_risk_df, aes(y = y, x = x, fill = count_2C)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "RedOr", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("+2°C by 2080-2100")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

PDSI_risk_4C_plot <- ggplot() +
  geom_raster(data = PDSI_risk_df, aes(y = y, x = x, fill = count_4C)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "RedOr", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("+4°C by 2080-2100")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

sp_2C_plot <- PDSI_risk_df %>%
  dplyr::mutate(risk_2C_cat = case_when(
    risk_2C == 0 ~ '0',
    risk_2C > 0 & risk_2C <=1 ~ '<1',
    risk_2C > 1 & risk_2C < 5 ~ '>1 to 5',
    risk_2C >= 5 & risk_2C < 10 ~ '>5 to 10',
    risk_2C >= 10 & risk_2C < 20 ~ '>10 to 20',
    risk_2C >= 20 & risk_2C < 100 ~ '>20 to 100',
    risk_2C >= 100 & risk_2C < 200 ~ '>100 to 200',
    risk_2C > 200 ~ '>200'),
    risk_2C = na_if(risk_2C, 0),
    risk_2C_cat = factor(risk_2C_cat,
                                   levels = c('>200','>100 to 200', '>20 to 100','>10 to 20', '>5 to 10', '>1 to 5', '<1'))) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = risk_2C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "Heat", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

sp_4C_plot <- PDSI_risk_df %>%
  dplyr::mutate(risk_4C_cat = case_when(
    risk_4C == 0 ~ '0',
    risk_4C > 0 & risk_4C <=1 ~ '<1',
    risk_4C > 1 & risk_4C < 5 ~ '>1 to 5',
    risk_4C >= 5 & risk_4C < 10 ~ '>5 to 10',
    risk_4C >= 10 & risk_4C < 20 ~ '>10 to 20',
    risk_4C >= 20 & risk_4C < 100 ~ '>20 to 100',
    risk_4C >= 100 & risk_4C < 200 ~ '>100 to 200',
    risk_4C > 200 ~ '>200'),
    risk_4C = na_if(risk_4C, 0),
    risk_4C_cat = factor(risk_4C_cat,
                                   levels = c('>200','>100 to 200', '>20 to 100','>10 to 20', '>5 to 10', '>1 to 5', '<1'))) %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = risk_4C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_discrete_sequential(palette = "Heat", rev = F) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

cowplot::plot_grid(PDSI_risk_2C_plot + theme(legend.position = "none"),
                   PDSI_risk_4C_plot + theme(legend.position = "none"),
                   sp_2C_plot + theme(legend.position = "none"),
                   sp_4C_plot + theme(legend.position = "none"),
                   ncol = 2,
                   align = "h", axis = "bt", labels = c('a', 'b', 'c', 'd'))

#PDSI_risk_df %>%
  #group_by(count_2C) %>%
  #summarise(length = length(count_2C)) %>%
  #mutate(freq = length / length(PDSI_risk_df$count_2C) * 100)

#PDSI_risk_df %>%
  #group_by(count_4C) %>%
  #summarise(length = length(count_4C)) %>%
  #mutate(freq = length / length(PDSI_risk_df$count_4C) * 100)
``` 

## Figure 3 - Water loss {-}

```{r Fig 3, fig.align='center', fig.height=4, fig.width=7, fig.show='hide'}
# Fig. 3a - EWL
lnVPD_conditions <- list(lnVPD = setNames(c(-4.60517, -2.302585, 0, 2.302585), 
                                        c(round(exp(-4.60517), digits = 2), 
                                          round(exp(-2.302585), digits = 1), 
                                          exp(0), 
                                          round(exp(2.302585), digits = 1))))

ewl_ce <- conditional_effects(ewl_model, effects = "lnMass:lnVPD", int_conditions = lnVPD_conditions)

ewl_ce <- data.frame(ewl_ce[[1]]) %>%
  dplyr::rename(estimate = estimate__,
                VPD_cal  = effect2__) %>%
  dplyr::mutate(mean_mass_g = exp(lnMass),
         VPD_kPa     = exp(lnVPD),
         VPD_cal     = as.numeric(as.character(VPD_cal)))

ewl_mass_plot <- ewl_dat %>%
  ggplot() +
  geom_point(aes(x = mean_mass_g, y = mg_h_mean, shape = strategy), size = 2, colour = "grey") +
  geom_line(data = ewl_ce, aes(x = mean_mass_g, y = exp(estimate), group = lnVPD, colour = lnVPD), size = 1) +
  geom_text(data = ewl_ce %>% filter(mean_mass_g == last(mean_mass_g)), 
            aes(label = VPD_cal, x = mean_mass_g + 50, y = exp(estimate), hjust = -0.01),
            size = 3) +
  labs(x = "Body mass (g)", colour = "VPD (kPa)", shape = NULL) +
  ylab(expression("EWL"~("mg"~H[2]*O~h^{"-1"}))) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(0.1, 1, 10, 100, 1000), labels = c(0.1, 1, 10, 100, "1,000")) +
  scale_x_continuous(trans = 'log10') +
  expand_limits(x = 500) +
  scale_colour_gradient2(low = "#255668", mid = "#17A77E", high = "#82CC6C", 
                         midpoint = -1,
                         breaks = c(-4.60517, -2.302585, 0, 2.302585), 
                         labels = c(round(exp(-4.60517), digits = 2), 
                                     round(exp(-2.302585), digits = 1), 
                                     exp(0), 
                                     round(exp(2.302585), digits = 1))) +
  scale_shape_manual(values = c(16, 1, 2, 0)) +
  mytheme() + theme(legend.position = "right") +
  guides(colour = guide_colourbar(barheight = 5, barwidth = 0.5, label.position = "right"))

# Fig. 3b
# VPD
VPD_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_vpd.nc')), anuran_sr) # VPD year 1981-2010 (kPa)
VPD_mean_rast <- raster::calc(VPD_rast, fun = mean, na.rm = TRUE) 
names(VPD_mean_rast) <-"VPD"

# Wind speed
ws_rast <- raster::projectRaster(raster::stack(x = file.path(spatial_path, 'TerraClimate19812010_ws.nc')), anuran_sr) # Wind speed year 1981-2010 (m/s)
ws_mean_rast <- raster::calc(ws_rast, fun = mean, na.rm = TRUE) 
names(ws_mean_rast) <-"wind_speed"

EWL_risk_rob <- raster::projectRaster(raster::stack(VPD_mean_rast, ws_mean_rast,
                                                    resample(anuran_sr, VPD_mean_rast)), 
                                       crs = rob_proj)

# Estimate EWL for a 30 g ground-dwelling frog
VPD_sp_df <- raster::as.data.frame(raster::rasterToPoints(EWL_risk_rob)) %>% 
  rename(species_n = layer) %>%
  drop_na(species_n) %>%
  dplyr::mutate(wind_speed_cm_s = wind_speed * 100, # from m/s to cm/s
                u = 0.4 * wind_speed_cm_s / log((100 / 0.15) + 1), # u = friction velocity
                wind_speed_corr = 2.5 * u * log((1 / 0.15) + 1), # correct for reference height (10 m) to ground level (1 cm) assuming level ground
                EWL_pred = 3.74 - 0.11 + (log(Ww_g) * 0.55) + (log(VPD) * 0.28) + (log(wind_speed_corr) * 0.34),
                EWL_pred = exp(EWL_pred) * 0.001 # from mg/h to g/h
                )

VPD_sp_plot <- VPD_sp_df %>%
  ggplot() +
  geom_raster(aes(y = y, x = x, fill = EWL_pred)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_continuous_sequential(palette = "YlGnBu", rev = T, 
                                   name = expression("EWL"~("g"~H[2]*O~h^{"-1"}))) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  guides(fill = guide_colourbar(barwidth = 20, barheight = 0.2, 
                                label.position = "bottom")) +
  theme_void() + ylab(NULL) + xlab(NULL) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10),
        legend.title = element_text(size = 8), 
        legend.position = "bottom") +
  coord_fixed(ratio = 1) # fixed ratio

cowplot::plot_grid(ewl_mass_plot,
                   VPD_sp_plot,
                   ncol = 1,
                   align = "h", axis = "bt", labels = c('a', 'b'), rel_heights = c(0.8, 1))
```

## Figure 4 - Activity risk {-}

``` {r Fig 4, fig.align='center', fig.height=7, fig.width=8, fig.show='hide'}
shad_plot <- shad_model %>%
  ggplot(aes(x = day, y = condition, fill = hours)) + 
  geom_tile() +
  scale_fill_continuous_diverging(palette = "Blue-Red 3", mid = 12, rev = TRUE) +
  ylab(NULL) + xlab("Day of the year") + 
  scale_x_continuous(expand = c(0, 0)) +
  mytheme() + theme(legend.position = "bottom") +
  guides(fill = guide_colourbar(barheight = 0.5, barwidth = 5, label.position = "bottom")) 

tree_plot <- tree_model %>%
  ggplot(aes(x = day, y = condition, fill = hours)) + 
  geom_tile() +
  scale_fill_continuous_diverging(palette = "Blue-Red 3", mid = 12, rev = TRUE) +
  ylab(NULL) + xlab("Day of the year") + 
  scale_x_continuous(expand = c(0, 0)) +
  mytheme() + theme(legend.position = "bottom") +
  guides(fill = guide_colourbar(barheight = 0.5, barwidth = 5, label.position = "bottom")) 

burr_plot <- burr_model %>%
  ggplot(aes(x = day, y = condition, fill = hours)) + 
  geom_tile() +
  scale_fill_continuous_diverging(palette = "Blue-Red 3", mid = 12, rev = TRUE) +
  ylab(NULL) + xlab("Day of the year") +
  scale_x_continuous(expand = c(0, 0)) +
  mytheme() + theme(legend.position = "bottom") +
  guides(fill = guide_colourbar(barheight = 0.5, barwidth = 5, label.position = "bottom")) 

prow_2 <- cowplot::plot_grid(
  shad_plot + theme(legend.position ="none"), 
  tree_plot + theme(legend.position ="none"), 
  burr_plot + theme(legend.position ="none"),
  ncol = 1, labels = c('a', 'b', 'c'),
  align = 'v', axis = 'l')

legend_b_2 <- cowplot::get_legend(burr_plot + guides(color = guide_legend(nrow = 1)))

plot_grid(prow_2, legend_b_2, nrow = 1, rel_heights = c(1, .1))
```

***

# Extended data {-}

## Extended Figure 1 - PDSI intensity risk {-}

```{r Fig E1, fig.align='center', fig.height=5, fig.width=9}
PDSI_2C_diff_rast_crop <- raster::mask(crop(PDSI_2C_diff_rast, extent(world)), world) # crop 
PDSI_2C_rob <- raster::projectRaster(raster::stack(PDSI_2C_diff_rast_crop, resample(anuran_sr, PDSI_2C_diff_rast_crop)), 
                          crs = rob_proj)

PDSI_4C_diff_rast_crop <- raster::mask(crop(PDSI_4C_diff_rast, extent(world)), world) # crop 
PDSI_4C_rob <- raster::projectRaster(raster::stack(PDSI_4C_diff_rast_crop, resample(anuran_sr, PDSI_4C_diff_rast_crop)), 
                          crs = rob_proj)

PDSI_2C_df_rob <- raster::as.data.frame(raster::rasterToPoints(PDSI_2C_rob)) %>%
  dplyr::rename(layer = delta_int_2C,
                species_n = layer) %>% 
  dplyr::mutate(change = case_when(
    layer >= 4 ~ "4", 
    layer >= 3 & layer < 4 ~ '3',
    layer >= 2 & layer < 3 ~ '2',  
    layer >= 1 & layer < 2 ~ '1',  
    layer >= -1 & layer < 1 ~ '0',
    layer >= -2 & layer < -1 ~ '-1',
    layer >= -3 & layer < -2 ~ '-2',
    layer >= -4 & layer < -3 ~ '-3',   
    layer < -4 ~ '-4' 
  )) %>% 
  dplyr::mutate(change = factor(change,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE)) %>%
  dplyr::filter(change != "NA" & species_n != "NA")

PDSI_4C_df_rob <- raster::as.data.frame(raster::rasterToPoints(PDSI_4C_rob)) %>%
  dplyr::rename(layer = delta_int_4C,
                species_n = layer) %>% 
  dplyr::mutate(change = case_when(
    layer >= 4 ~ "4", 
    layer >= 3 & layer < 4 ~ '3',
    layer >= 2 & layer < 3 ~ '2',  
    layer >= 1 & layer < 2 ~ '1',  
    layer >= -1 & layer < 1 ~ '0',
    layer >= -2 & layer < -1 ~ '-1',
    layer >= -3 & layer < -2 ~ '-2',
    layer >= -4 & layer < -3 ~ '-3',   
    layer < -4 ~ '-4' 
  )) %>% 
  dplyr::mutate(change = factor(change,
                                levels = c('-4', '-3', '-2', '-1', '0', '1', '2', '3', '4'),
                                ordered = TRUE)) %>%
  dplyr::filter(change != "NA" & species_n != "NA")

# Intensity +2C - Ex Fig. 1a
colours_PDSI <- RColorBrewer::brewer.pal(9, "RdBu")
PDSI_2C_plot <- ggplot() +
  geom_raster(data = PDSI_2C_df_rob, aes(y = y, x = x, fill = change)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = colours_PDSI) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression(Delta*"PDSI intensity (+2°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

# Intensity +4C - Ex Fig. 1c
PDSI_4C_plot <- ggplot() +
  geom_raster(data = PDSI_4C_df_rob, aes(y = y, x = x, fill = change)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = colours_PDSI) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression(Delta*"PDSI intensity (+4°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

# 2C change - Ex Fig 1b
PDSI_sp_2C_plot <- PDSI_sp_2C %>%
  rows_insert(tibble(change_2C = "4", all_freq = 0), conflict = "ignore") %>%
  filter(change_2C != "NA") %>%
  ggplot(aes(x = change_2C, y = all_freq, fill = change_2C)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = colours_PDSI) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("PDSI") +
  ylim(0, 80) +
  ggtitle("Grid cell occupied +2°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

# 4C change - Ex Fig 1d
PDSI_sp_4C_plot <- PDSI_sp_4C %>%
  filter(change_4C != "NA") %>%
  ggplot(aes(x = change_4C, y = all_freq, fill = change_4C)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = colours_PDSI) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("PDSI") +
  ylim(0, 80) +
  ggtitle("Grid cell occupied +4°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

left_plot <- cowplot::plot_grid(
  PDSI_2C_plot + theme(legend.position = "none"),
  PDSI_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('a', 'c', 'e'))

right_plot <- cowplot::plot_grid(
  PDSI_sp_2C_plot + theme(legend.position = "none"),
  PDSI_sp_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('b', 'd', 'f'))

cowplot::plot_grid(left_plot, right_plot, ncol = 2, rel_widths = c(1, 0.7))
```

## Extended Figure 2 - PDSI frequency risk {-}

```{r Fig E2, fig.align='center', fig.height=5, fig.width=9}
freq_diff_rast_crop <- raster::mask(crop(freq_diff_rast, extent(world)), world) # crop

PDSI_freq_rob <- raster::projectRaster(raster::stack(freq_diff_rast_crop, 
                                           resample(anuran_sr, freq_diff_rast_crop)), 
                             crs = rob_proj)
                             
PDSI_freq_2C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_freq_rob)) %>%
  dplyr::mutate(diff_2C_cat = case_when(
    diff_2C >= 10 ~ "10-12", 
    diff_2C >= 8 & diff_2C < 10 ~ '8-10',
    diff_2C >= 6 & diff_2C < 8 ~ '6-8',  
    diff_2C >= 4 & diff_2C < 6 ~ '4-6',  
    diff_2C >= 2 & diff_2C < 4 ~ '2-4',
    diff_2C >= 1 & diff_2C < 2 ~ '1-2',
    diff_2C > 0 & diff_2C < 1 ~ '0-1',
    diff_2C >= -1 & diff_2C < 0 ~ '-0-1',
    diff_2C >= -2 & diff_2C < -1 ~ '-1-2',
    diff_2C >= -4 & diff_2C < -2 ~ '-2-4'
  )) %>% 
  dplyr::mutate(diff_2C_cat = factor(diff_2C_cat,
                                     levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1','-0-1', '-1-2', '-2-4'),
                                ordered = TRUE)) %>%
  dplyr::filter(diff_2C_cat != "NA" & layer != "NA")

PDSI_freq_4C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_freq_rob)) %>%
  dplyr::mutate(diff_4C_cat = case_when(
    diff_4C >= 10 ~ "10-12", 
    diff_4C >= 8 & diff_4C < 10 ~ '8-10',
    diff_4C >= 6 & diff_4C < 8 ~ '6-8',  
    diff_4C >= 4 & diff_4C < 6 ~ '4-6',  
    diff_4C >= 2 & diff_4C < 4 ~ '2-4',
    diff_4C >= 1 & diff_4C < 2 ~ '1-2',
    diff_4C > 0 & diff_4C < 1 ~ '0-1',
    diff_4C >= -1 & diff_4C < 0 ~ '-0-1',
    diff_4C >= -2 & diff_4C < -1 ~ '-1-2',
    diff_4C >= -4 & diff_4C < -2 ~ '-2-4'
  )) %>% 
  dplyr::mutate(diff_4C_cat = factor(diff_4C_cat,
                                     levels = c('10-12', '8-10', '6-8', '4-6', '2-4', '1-2', '0-1', '-0-1', '-1-2', '-2-4'),
                                     ordered = TRUE)) %>%
  dplyr::filter(diff_4C_cat != "NA" & layer != "NA")

# Freq map 2C - Fig S10a
freq_2C_plot <- ggplot() +
  geom_raster(data = PDSI_freq_2C_df, aes(y = y, x = x, fill = diff_2C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in drought frequency ("*Delta*"PDSI +2°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

# Freq map 4C - Fig S10c
freq_4C_plot <- ggplot() +
  geom_raster(data = PDSI_freq_4C_df, aes(y = y, x = x, fill = diff_4C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in drought frequency ("*Delta*"PDSI +4°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

# 2C change - Fig S10b
freq_sp_2C <- data.frame(PDSI_freq_2C_df %>% 
                           dplyr::group_by(diff_2C_cat) %>% 
                           dplyr::summarise(species_n = length(layer[!is.na(layer)]))) %>%
  dplyr::mutate(all_freq  = species_n / sum(species_n) * 100)

freq_sp_2C_plot <- freq_sp_2C %>%
  ggplot(aes(x = diff_2C_cat, y = all_freq, fill = diff_2C_cat)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("Months") +
  ylim(0, 60) +
  ggtitle("Grid cell occupied +2°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

# 4C change - Fig S10d
freq_sp_4C <- data.frame(PDSI_freq_4C_df %>% dplyr::group_by(diff_4C_cat) %>%
                           dplyr::summarise(species_n = length(layer[!is.na(layer)]))) %>%
  dplyr::mutate(all_freq  = species_n / sum(species_n) * 100)

freq_sp_4C_plot <- freq_sp_4C %>%
  ggplot(aes(x = diff_4C_cat, y = all_freq, fill = diff_4C_cat)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2")) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("Months") +
  ylim(0, 60) +
  ggtitle("Grid cell occupied +4°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

left_plot2 <- cowplot::plot_grid(
  freq_2C_plot + theme(legend.position = "none"),
  freq_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('a', 'c'))

right_plot2 <- cowplot::plot_grid(
  freq_sp_2C_plot + theme(legend.position = "none"),
  freq_sp_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('b', 'd'))

cowplot::plot_grid(left_plot2, right_plot2, ncol = 2, rel_widths = c(1, 0.7))
```

## Extended Figure 3 - PDSI duration risk {-}

```{r Fig E3, fig.align='center', fig.height=5, fig.width=9}
PDSI_dur_diff_crop <- raster::mask(crop(PDSI_dur_diff, extent(world)), world) # crop

PDSI_dur_rob <- raster::projectRaster(raster::stack(PDSI_dur_diff_crop, 
                                           resample(anuran_sr, PDSI_dur_diff_crop)), 
                             crs = rob_proj)

PDSI_dur_2C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_dur_rob)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(diff_2C_cat = case_when(
    delta_dur_2C >= 10 ~ ">10", 
    delta_dur_2C >= 8 & delta_dur_2C < 10 ~ '8-10',
    delta_dur_2C >= 6 & delta_dur_2C < 8 ~ '6-8',  
    delta_dur_2C >= 4 & delta_dur_2C < 6 ~ '4-6',  
    delta_dur_2C >= 2 & delta_dur_2C < 4 ~ '2-4',
    delta_dur_2C >= 1 & delta_dur_2C < 2 ~ '1-2',
    delta_dur_2C > 0 & delta_dur_2C < 1 ~ '0-1',
    delta_dur_2C >= -1 & delta_dur_2C < 0 ~ '-0-1',
    delta_dur_2C >= -2 & delta_dur_2C < -1 ~ '-1-2',
    delta_dur_2C >= -4 & delta_dur_2C < -2 ~ '-2-4',
    delta_dur_2C >= -8 & delta_dur_2C < -6 ~ '-6-8',
    delta_dur_2C >= -10 & delta_dur_2C < -8 ~ '-8-10',   
    delta_dur_2C < -10 ~ '<-10' 
  )) %>% 
  dplyr::mutate(diff_2C_cat = factor(diff_2C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', "0-1", '-0-1', '-1-2', '-2-4', '-4-6', '-6-8', '-8-10', '<-10'),
                                ordered = TRUE)) %>%
  dplyr::filter(diff_2C_cat != "NA")

PDSI_dur_4C_df <- raster::as.data.frame(raster::rasterToPoints(PDSI_dur_rob)) %>%
  dplyr::rename("sp_n" = layer) %>%
  dplyr::filter(!is.na(sp_n)) %>%
  dplyr::mutate(diff_4C_cat = case_when(
    delta_dur_4C >= 10 ~ ">10", 
    delta_dur_4C >= 8 & delta_dur_4C < 10 ~ '8-10',
    delta_dur_4C >= 6 & delta_dur_4C < 8 ~ '6-8',  
    delta_dur_4C >= 4 & delta_dur_4C < 6 ~ '4-6',  
    delta_dur_4C >= 2 & delta_dur_4C < 4 ~ '2-4',
    delta_dur_4C >= 1 & delta_dur_4C < 2 ~ '1-2',
    delta_dur_4C > 0 & delta_dur_4C < 1 ~ '0-1',
    delta_dur_4C >= -1 & delta_dur_4C < 0 ~ '-0-1',
    delta_dur_4C >= -2 & delta_dur_4C < -1 ~ '-1-2',
    delta_dur_4C >= -4 & delta_dur_4C < -2 ~ '-2-4',
    delta_dur_4C >= -6 & delta_dur_4C < -4 ~ '-4-6',
    delta_dur_4C >= -8 & delta_dur_4C < -6 ~ '-6-8',
    delta_dur_4C >= -10 & delta_dur_4C < -8 ~ '-8-10',   
    delta_dur_4C < -10 ~ '<-10' 
  )) %>% 
  dplyr::mutate(diff_4C_cat = factor(diff_4C_cat,
                                levels = c('>10', '8-10', '6-8', '4-6', '2-4', '1-2', "0-1", '-0-1', '-1-2', '-2-4', '-4-6', '-6-8', '-8-10', '<-10'),
                                ordered = TRUE)) %>%
  dplyr::filter(diff_4C_cat != "NA")

dur_2C_plot <- ggplot() +
  geom_raster(data = PDSI_dur_2C_df, aes(y = y, x = x, fill = diff_2C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in drought duration ("*Delta*"PDSI +2°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

dur_4C_plot <- ggplot() +
  geom_raster(data = PDSI_dur_4C_df, aes(y = y, x = x, fill = diff_4C_cat)) +
  geom_polygon(data = world_rob, aes(x = long, y = lat, group = group), colour = "#64686b", fill = NA, size = 0.1) +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
  ggnewscale::new_scale("fill") +
  geom_raster(data = hill_df %>% filter(hill <= 0.645), aes(lon, lat, fill = hill, alpha = hill), show.legend = FALSE) +
  scale_fill_gradient(low = "black", high = "grey") +
  scale_alpha_continuous(trans = "reverse") +
  theme_void() + ylab(NULL) + xlab(NULL) +
  ggtitle(expression("Change in drought duration ("*Delta*"PDSI +4°C)")) +
  scale_y_continuous(limits = c(-61e5, 85e5), expand = c(0, 0)) +
  scale_x_continuous(limits = c(-15e6, 16e6), expand = c(0, 0)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        plot.title = element_text(size = 10)) +
  coord_fixed(ratio = 1) # fixed ratio

# 2C change - Fig S11b
dur_sp_2C <- data.frame(PDSI_dur_2C_df %>% dplyr::group_by(diff_2C_cat) %>% 
                            dplyr::summarise(species_n = length(sp_n[!is.na(sp_n)]))) %>%
  dplyr::mutate(all_freq  = species_n / sum(species_n) * 100)

dur_sp_2C_plot <- dur_sp_2C %>%
  ggplot(aes(x = diff_2C_cat, y = all_freq, fill = diff_2C_cat)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("Months") +
  ylim(0, 50) +
  ggtitle("Grid cell occupied +2°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

# 4C change - Fig S11d
dur_sp_4C <- data.frame(PDSI_dur_4C_df %>% dplyr::group_by(diff_4C_cat) %>%
                           dplyr::summarise(species_n = length(sp_n[!is.na(sp_n)]))) %>%
  dplyr::mutate(all_freq  = species_n / sum(species_n) * 100)

dur_sp_4C_plot <- dur_sp_4C %>%
  ggplot(aes(x = diff_4C_cat, y = all_freq, fill = diff_4C_cat)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("#67001F", "#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#FAE9DF", "#F7F7F7", "#dfebf2", "#D1E5F0")) +
  geom_text(aes(label = round(all_freq, 1)), vjust = -1, size = 2) +
  ylab("% species") + xlab("Months") +
  ylim(0, 50) +
  ggtitle("Grid cell occupied +4°C") +
  mytheme() + 
  theme(plot.title = element_text(size = 10))

left_plot3 <- cowplot::plot_grid(
  dur_2C_plot + theme(legend.position = "none"),
  dur_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('a', 'c'))

right_plot3 <- cowplot::plot_grid(
  dur_sp_2C_plot + theme(legend.position = "none"),
  dur_sp_4C_plot + theme(legend.position = "none"),
  ncol = 1,
  align = "h", axis = "bt", labels = c('b', 'd'))

cowplot::plot_grid(left_plot3, right_plot3, ncol = 2, rel_widths = c(1, 0.7))
```

## Extended Figure 4 - Water uptake {-}

```{r Fig E4, fig.align='center', fig.height=3, fig.width=5}
hydr_conditions <- list(hydration = setNames(c(70, 80, 90, 100), c(70, 80, 90, 100)))
wu_ce <- conditional_effects(wu_model, effects = "lnMass:hydration", int_conditions = hydr_conditions)

wu_ce <- data.frame(wu_ce[[1]]) %>%
  dplyr::rename(estimate = estimate__,
                hydration_cal = effect2__) %>%
  dplyr::mutate(mean_mass_g = exp(lnMass),
                hydration_cal = as.numeric(as.character(hydration_cal)))

wu_dat %>%
  ggplot() +
  geom_point(aes(x = mean_mass_g, y = mg_h_mean),  size = 2, colour = "grey") +
  geom_line(data = wu_ce, aes(x = mean_mass_g, y = exp(estimate), group = hydration, colour = hydration), size = 1) +
  geom_text(data = wu_ce %>% filter(mean_mass_g == last(mean_mass_g)), 
            aes(label = hydration, 
                x = mean_mass_g + 50, 
                y = exp(estimate),
                hjust = -0.01),
            size = 3) +
  labs(x = "Body mass (g)", colour = "Initial hydration (%)") +
  ylab(expression("WU"~("mg"~H[2]*O~h^{"-1"}))) +
  scale_y_continuous(trans = scales::log_trans(), breaks = c(10, 100, 1000, 10000, 100000), labels = c(10, 100, "1,000", "10,000", "100,000")) +
  scale_x_continuous(trans = 'log10') +
  scale_colour_gradient2(low = "#0E3F5C", mid = "#2A6D7A", high = "#8FCCB4", 
                         midpoint = 85) +
  expand_limits(x = 500) +
  mytheme() + 
  theme(legend.title = element_text(size = 8), 
        legend.position = "bottom") +
  guides(colour = guide_colourbar(barheight = 0.5, barwidth = 10, title.position = "top"))
```

***

# References {-}

<div id="refs"></div>
<br>

***

## Session Information {-}

```{r sessioninfo, echo = FALSE}
pander::pander(sessionInfo(), locale = FALSE)
```